video steganography thesis

From Covert Hiding to Visual Editing: Robust Generative Video Steganography

Traditional video steganography methods are based on modifying the covert space for embedding, whereas we propose an innovative approach that embeds secret message within semantic feature for steganography during the video editing process. Although existing traditional video steganography methods display a certain level of security and embedding capacity, they lack adequate robustness against common distortions in online social networks (OSNs). In this paper, we introduce an end-to-end robust generative video steganography network (RoGVS), which achieves visual editing by modifying semantic feature of videos to embed secret message. We employ face-swapping scenario to showcase the visual editing effects. We first design a secret message embedding module to adaptively hide secret message into the semantic feature of videos. Extensive experiments display that the proposed RoGVS method applied to facial video datasets demonstrate its superiority over existing video and image steganography techniques in terms of both robustness and capacity.

Index Terms —  Generative video steganography, Robust steganography, Semantic modification

1 Introduction

Steganography is the science and technology of embedding secret message into natural digital carriers, such as image, video, text, etc. Generally, the natural digital carriers are called “cover” and the digital media with secret message are called “stego”. Conventional image steganography methods [ 49 , 12 , 31 ] primarily modify high-frequency components to embed secret message. They commonly utilize methodologies such as pixel value manipulation or integrating secret message into the cover image before inputting it into an encoder for steganographic purposes.

In the past few years, as the rise of short video software applications like TikTok, YouTube, Snapchat, etc., video has become a suitable carrier for steganography. Traditional video steganographic methods, utilizing direct pixel value manipulation [ 32 ] , coding mapping [ 34 ] , or adaptive distortion function [ 36 ] , exploit video data redundancy for information hiding. Nevertheless, while successful in security and embedding capacity, these methods on modifying covert space can be erased by common post-processing operations easily. So they are vulnerable to mitigate diverse distortions that may occur in lossy channel transmission.

Visual editing on videos can be seen as the process of modifying the semantic information of objects within them. Instead of hiding secret message in covert space, we embed secret message within semantic feature of videos for visual edition. The advanced semantic feature is less susceptible to distortions, making this method inherently robust. In order to improve the robustness of video steganography, we propose an end-to-end robust generative video steganography network (RoGVS), which consists of four modules, containing information encoding module, secret message embedding model, attacking layer, and secret message extraction module. For evaluation, we use face-swapping technology as an example to show the effectiveness of our method, while it can be easily extended to other applications. Comprehensive experiments have showcased that our method surpasses state-of-the-art techniques, attaining commendable robustness and generalization capabilities.

The main contributions of our work are as follows: 1) We are the first to explore a novel generative video steganography method, which modifies semantic feature to embed secret message during visual editing instead of modify the covert space. This framework exhibits strong extensibility, serving as a new topic for the future development of the steganography field. 2) The proposed method is robust against common distortions in social network platform and the secret message can be extracted with high accuracy. 3) Our method achieves better security for anti-steganalysis than other state-of-the-art methods, which can effectively evade the detection of steganalysis system.

2 Related Work

Image Steganography. Conventional image steganography methods primarily modify high-frequency components to embed secret message. The LSB substitution method   [ 80 ] operates under the assumption that human eyes cannot perceive changes in the least significant bit of pixel values. HiDDeN   [ 12 ] introduces an end-to-end trainable framework through an encoder-decoder architecture. SteganoGAN   [ 31 ] employs dense encoders to enhance payload capacity. Wei et al   [ 16 ] propose an advanced generative steganography network that can generate realistic stego images without using cover images. However, alterations in high-frequency components can be obliterated by common post-processing operations, such as JPEG compression or Gaussian Blur.

Video Steganography. Early video steganography usually modifies RGB or YUV color spaces for embedding secret message. Dong et al [ 33 ] observed that altering intra-frame modes in HEVC significantly affected video coding efficiency, while modifications to multilevel recursive coding units had minimal distortion impact. PWRN [ 35 ] employs a super-resolution CNN, the Wide Residual-Net filter (PWRN), to replace HEVC’s loop filter. Recently, He et al [ 36 ] devised an adaptive distortion function using enhanced Rate Distortion Optimization (RDO) and Syndrome-Trellis Code (STC) to minimize embedding distortion. However, these methods are struggle to handle various distortions that may arise in lossy channel transmission.

Visual Editing. Visual editing can encompass color correction on a single image, deletion, addition, or alteration of objects within the image, or even merging two photos to create an entirely new scene. In videos, visual editing might involve adding effects to specific frames, removing elements from the video to alter the scene, replacing one person’s face with another   [ 26 ] , also called face-swapping.

3 Proposed Approach

Our method aims to embed secret message 𝑴 𝑴 \bm{M} bold_italic_M using semantic feature extracted from reference image 𝑰 R subscript 𝑰 𝑅 \bm{I}_{R} bold_italic_I start_POSTSUBSCRIPT italic_R end_POSTSUBSCRIPT into cover video 𝑽 C subscript 𝑽 𝐶 \bm{V}_{C} bold_italic_V start_POSTSUBSCRIPT italic_C end_POSTSUBSCRIPT , generating stego video 𝑽 C ′ superscript subscript 𝑽 𝐶 ′ \bm{V}_{C}^{{}^{\prime}} bold_italic_V start_POSTSUBSCRIPT italic_C end_POSTSUBSCRIPT start_POSTSUPERSCRIPT start_FLOATSUPERSCRIPT ′ end_FLOATSUPERSCRIPT end_POSTSUPERSCRIPT . As illustrated in Fig. 2 , our approach comprises four modules: Information Encoding Module, Secret Message Embedding Module, Attacking Layer, Secret Message Extraction Module.

3.1 Information Encoding Module

The information encoding module consists of three parts: The first is identity extractor ( 𝑬 i ⁢ d ) subscript 𝑬 𝑖 𝑑 \left(\bm{E}_{id}\right) ( bold_italic_E start_POSTSUBSCRIPT italic_i italic_d end_POSTSUBSCRIPT ) which utilizes a facial recognition network to extract identity feature tailored for the reference image ( 𝑰 R ) subscript 𝑰 𝑅 \left(\bm{I}_{R}\right) ( bold_italic_I start_POSTSUBSCRIPT italic_R end_POSTSUBSCRIPT ) . The second is video feature extractor ( 𝑬 ϕ ) subscript 𝑬 italic-ϕ \left(\bm{E}_{\phi}\right) ( bold_italic_E start_POSTSUBSCRIPT italic_ϕ end_POSTSUBSCRIPT ) . It acquires the latent representation of cover video 𝑽 C subscript 𝑽 𝐶 \bm{V}_{C} bold_italic_V start_POSTSUBSCRIPT italic_C end_POSTSUBSCRIPT with v 𝑣 v italic_v frames, employing an encoder [ 26 ] for video feature extraction. The third is secret message encoder ( 𝑬 m ) subscript 𝑬 𝑚 \left(\bm{E}_{m}\right) ( bold_italic_E start_POSTSUBSCRIPT italic_m end_POSTSUBSCRIPT ) which is a one-layer dense Multi-layer Perceptron (MLP). The above three parts are formulated as follows:

where 𝑰 C i subscript superscript 𝑰 𝑖 𝐶 \bm{I}^{i}_{C} bold_italic_I start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_C end_POSTSUBSCRIPT is the i 𝑖 i italic_i -th frame of the cover video. 𝑭 C i subscript superscript 𝑭 𝑖 𝐶 \bm{F}^{i}_{C} bold_italic_F start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_C end_POSTSUBSCRIPT represents the latent feature representation of i 𝑖 i italic_i -th frame. 𝑭 i ⁢ d subscript 𝑭 𝑖 𝑑 \bm{F}_{id} bold_italic_F start_POSTSUBSCRIPT italic_i italic_d end_POSTSUBSCRIPT is the identity feature of the reference image. 𝑴 𝑴 \bm{M} bold_italic_M is the secret message. 𝑾 𝒎 subscript 𝑾 𝒎 \bm{W_{m}} bold_italic_W start_POSTSUBSCRIPT bold_italic_m end_POSTSUBSCRIPT and 𝒃 m subscript 𝒃 𝑚 \bm{b}_{m} bold_italic_b start_POSTSUBSCRIPT italic_m end_POSTSUBSCRIPT represents the learnable weights and biases.

3.2 Secret Message Embedding and Extraction Module

This module aims to embed the secret message during face swapping. The key problem is how to implement face swapping under the guidance of secret message. To our understanding, the latent features of the cover video encompass both identity and attribute feature. Face swapping essentially involves replacing the cover video’s identity with that of the reference image. Consequently, we embed the secret message into the identity feature of the reference image, formulated as follows:

where λ 𝜆 \lambda italic_λ is a hyper-parameter adjusting the influence of secret message on identity feature.

Due to strong coupling between identity and attribute features, direct extraction of attribute feature from the latent representation 𝑭 c i superscript subscript 𝑭 𝑐 𝑖 \bm{F}_{c}^{i} bold_italic_F start_POSTSUBSCRIPT italic_c end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT by 𝑬 ϕ subscript 𝑬 italic-ϕ \bm{E}_{\phi} bold_italic_E start_POSTSUBSCRIPT italic_ϕ end_POSTSUBSCRIPT is unfeasible. To ensure better attribute preservation, we design a Secret-ID block, consisting of the modified version of the residual block and AdaIN to inject 𝑭 i ⁢ d i superscript subscript 𝑭 𝑖 𝑑 𝑖 \bm{F}_{id}^{i} bold_italic_F start_POSTSUBSCRIPT italic_i italic_d end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT into 𝑭 C i superscript subscript 𝑭 𝐶 𝑖 \bm{F}_{C}^{i} bold_italic_F start_POSTSUBSCRIPT italic_C end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT . The Secret-ID block is formulated as follows:

where μ ⁢ ( 𝑭 C i ) 𝜇 subscript superscript 𝑭 𝑖 𝐶 \mu({\bm{F}^{i}_{C}}) italic_μ ( bold_italic_F start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_C end_POSTSUBSCRIPT ) and σ ⁢ ( 𝑭 C i ) 𝜎 subscript superscript 𝑭 𝑖 𝐶 \sigma({\bm{F}^{i}_{C}}) italic_σ ( bold_italic_F start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_C end_POSTSUBSCRIPT ) represent the channel-wise mean and standard deviation of the input feature 𝑭 C i subscript superscript 𝑭 𝑖 𝐶 \bm{F}^{i}_{C} bold_italic_F start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_C end_POSTSUBSCRIPT , respectively. Meanwhile, σ 𝑭 i ⁢ d ′ subscript 𝜎 subscript superscript 𝑭 ′ 𝑖 𝑑 \sigma_{\bm{F}^{\prime}_{id}} italic_σ start_POSTSUBSCRIPT bold_italic_F start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i italic_d end_POSTSUBSCRIPT end_POSTSUBSCRIPT and μ 𝑭 i ⁢ d ′ subscript 𝜇 subscript superscript 𝑭 ′ 𝑖 𝑑 \mu_{\bm{F}^{\prime}_{id}} italic_μ start_POSTSUBSCRIPT bold_italic_F start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i italic_d end_POSTSUBSCRIPT end_POSTSUBSCRIPT correspond to two variables derived from the secret-identity feature 𝑭 i ⁢ d ′ subscript superscript 𝑭 ′ 𝑖 𝑑 \bm{F}^{\prime}_{id} bold_italic_F start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i italic_d end_POSTSUBSCRIPT .

After N Secret-ID blocks, the identity feature in 𝑭 C i superscript subscript 𝑭 𝐶 𝑖 \bm{F}_{C}^{i} bold_italic_F start_POSTSUBSCRIPT italic_C end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT is replaced by 𝑭 i ⁢ d ′ subscript superscript 𝑭 ′ 𝑖 𝑑 \bm{F}^{\prime}_{id} bold_italic_F start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i italic_d end_POSTSUBSCRIPT and then we get 𝑭 S i subscript superscript 𝑭 𝑖 𝑆 \bm{F}^{i}_{S} bold_italic_F start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT . Subsequently, we use an video Decoder 𝑫 ϕ subscript 𝑫 italic-ϕ \bm{D}_{\phi} bold_italic_D start_POSTSUBSCRIPT italic_ϕ end_POSTSUBSCRIPT to recover the i 𝑖 i italic_i -th frame 𝑰 S i superscript subscript 𝑰 𝑆 𝑖 \bm{I}_{S}^{i} bold_italic_I start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT of the stego video from 𝑭 S i subscript superscript 𝑭 𝑖 𝑆 \bm{F}^{i}_{S} bold_italic_F start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT . The Decoder contains four upsample blocks, a ReflectionPad layer and a convolutional layer. Each upsample block consists of a upsample layer, a convolutional layer and a BatchNorm layer. The process to get 𝑰 S i subscript superscript 𝑰 𝑖 𝑆 \bm{I}^{i}_{S} bold_italic_I start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT can be expressed as 𝑰 S i = 𝑫 ϕ ⁢ ( 𝑭 S i ) subscript superscript 𝑰 𝑖 𝑆 subscript 𝑫 italic-ϕ subscript superscript 𝑭 𝑖 𝑆 \bm{I}^{i}_{S}=\bm{D}_{\phi}(\bm{F}^{i}_{S}) bold_italic_I start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT = bold_italic_D start_POSTSUBSCRIPT italic_ϕ end_POSTSUBSCRIPT ( bold_italic_F start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT ) .

We design an extraction module to retrieve secret message 𝑴 ′ superscript 𝑴 ′ \bm{M}^{\prime} bold_italic_M start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT from the stego videos, featuring seven convolutional layers using ReLU activation. Ultimately, a sigmoid activation function and binarization are applied to extract the embedded secret message. This module’s formulation is as 𝑴 ′ = 𝑬 e ⁢ x ⁢ t ⁢ ( 𝑽 S ) superscript 𝑴 ′ subscript 𝑬 𝑒 𝑥 𝑡 subscript 𝑽 𝑆 \bm{M}^{\prime}=\bm{E}_{ext}\left(\bm{V}_{S}\right) bold_italic_M start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT = bold_italic_E start_POSTSUBSCRIPT italic_e italic_x italic_t end_POSTSUBSCRIPT ( bold_italic_V start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT ) .

3.3 Attacking Layer

To bolster the robustness of our method for face-swapping videos in real-world scenarios, we design a attacking layer. This module simulates prevalent distortions encountered across social network platforms.

JPEG Compression. JPEG compression involves a non-differentiable quantization step due to rounding. To mitigate this, we apply Shin et al.’s method   [ 53 ] to approximate the near-zero quantization step using function Eq. ( 6 ):

where x 𝑥 x italic_x denotes pixels of the input image. We uniformly sample the JPEG quality from within the range of [50, 100].

Color Distortions. We consider two general color distortions: brightness and contrast. We perform a linear transformation on the pixels of each channel as the formula Eq. ( 7 ):

where 𝒑 ⁢ ( 𝒙 ) 𝒑 𝒙 \bm{p(x)} bold_italic_p bold_( bold_italic_x bold_) and 𝒇 ⁢ ( 𝒙 ) 𝒇 𝒙 \bm{f(x)} bold_italic_f bold_( bold_italic_x bold_) refers to the distorted and the original image. The parameters 𝒂 𝒂 \bm{a} bold_italic_a and 𝒄 𝒄 \bm{c} bold_italic_c regulate contrast and brightness, respectively.

Color Saturation. We perform random linear interpolation between RGB and gray images equivalent to simulate the distortion.

Additive Noise. We use Gaussian noise to simulate any other distortions that are not considered in the attacking layer. We employ a Gaussian noise model (sampling the standard deviation δ ∼ U [ 0 , 0.2 ] ) \delta\sim U[0,0.2]) italic_δ ∼ italic_U [ 0 , 0.2 ] ) to simulate imaging noise.

3.4 Loss Function

The proposed method ensures both high stego video quality and precise extraction of secret message. We achieve this by training the modules using the following losses.

Identity Loss . The identity loss minimizes the variance between the identity features ( 𝑭 i ⁢ d subscript 𝑭 𝑖 𝑑 \bm{F}_{id} bold_italic_F start_POSTSUBSCRIPT italic_i italic_d end_POSTSUBSCRIPT ) of the reference image and the i 𝑖 i italic_i -th frame ( 𝑭 ^ i ⁢ d i superscript subscript ^ 𝑭 𝑖 𝑑 𝑖 \hat{\bm{F}}_{id}^{i} over^ start_ARG bold_italic_F end_ARG start_POSTSUBSCRIPT italic_i italic_d end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT ) in the stego video, reducing alterations caused by secret message. Cosine similarity is used to measure this loss by the formula Eq ( 8 ).

Attribute Loss. We use the weak feature matching loss   [ 26 ] to constrain attribute difference before and after embedding secret message. The loss function is defined as follows:

where 𝑫 j subscript 𝑫 𝑗 \bm{D}_{j} bold_italic_D start_POSTSUBSCRIPT italic_j end_POSTSUBSCRIPT refers to the feature extractor of Discriminator D for the j-th layer, N j subscript 𝑁 𝑗 N_{j} italic_N start_POSTSUBSCRIPT italic_j end_POSTSUBSCRIPT is the number of elements in the j-th layer, and H 𝐻 H italic_H is the total number of layers. Additionally, h ℎ h italic_h represents the starting layer for computing the weak feature matching loss.

Adversarial Loss. To enhance performance, we use multi-scale Discriminator with gradient penalty. We adopt the Hinge version of adversarial loss defined as follows:

where 𝑫 𝑫 \bm{D} bold_italic_D denotes the Discriminator, x 𝑥 x italic_x and z 𝑧 z italic_z in our method is respectively 𝑰 R subscript 𝑰 𝑅 \bm{I}_{R} bold_italic_I start_POSTSUBSCRIPT italic_R end_POSTSUBSCRIPT and 𝑰 S i superscript subscript 𝑰 𝑆 𝑖 \bm{I}_{S}^{i} bold_italic_I start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_i end_POSTSUPERSCRIPT

Secret Loss. To address this, we use the Binary Cross-Entropy loss (BCE) as defined in Eq. ( 11 ).

Total loss. The total loss is defined as follows:

4 Experiments

4.1 experimental setups.

Datasets. We use Vggface2   [ 61 ] for training and FFHQ   [ 15 ] for validation. We crop and resize facial areas to a fixed 224 × \times × 224 resolution for input images. To analyze quality and performance, we randomly select 100 videos from DeepFake MNIST+   [ 65 ] to evaluate the performance.

Implementation Details. We train the model to encode a binary message of length m 𝑚 m italic_m = 9 or 18 bits in a frame. During training, we employ Adam optimizer with a learning rate of 4 × 10 − 4 4 superscript 10 4 4\times 10^{-4} 4 × 10 start_POSTSUPERSCRIPT - 4 end_POSTSUPERSCRIPT and a batch size of 4. We set α 1 = 10 subscript 𝛼 1 10 \alpha_{1}=10 italic_α start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT = 10 , α 2 = 10 subscript 𝛼 2 10 \alpha_{2}=10 italic_α start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT = 10 , α 3 = 15 subscript 𝛼 3 15 \alpha_{3}=15 italic_α start_POSTSUBSCRIPT 3 end_POSTSUBSCRIPT = 15 , and α 4 = 10 − 5 subscript 𝛼 4 superscript 10 5 \alpha_{4}=10^{-5} italic_α start_POSTSUBSCRIPT 4 end_POSTSUBSCRIPT = 10 start_POSTSUPERSCRIPT - 5 end_POSTSUPERSCRIPT . The networks train for 1 million steps, integrating the Attacking Layer after the initial 800k steps for stability. We use an NVIDIA GeForce RTX 3090 GPU for our experiments.

Evaluation Metrics. We employ Bits Per Frame (BPF), quantifying the bits number of secret message per frame in the stego video. To assess robustness, we evaluate secret message extraction accuracy under various scenarios. For security assessment, we use three steganalysis methods [ 62 , 63 , 64 ] to demonstrate our method’s anti-detection capability.

Baselines. To ensure fair comparison in our experiments, we align HiDDeN and LSB to this capacity. Detailed methods of HiDDeN and LSB are available in the supplementary materials. Additionally, due to its PU-based design, PWRN has a limited capacity of 15 BPF when resizing input images to 224 × 224 224 224 224\times 224 224 × 224 .

4.2 Performance Analysis

We compare the performance of our RoGVS with image-level steganography including HiDDeN  [ 12 ] and LSB  [ 80 ] and video-level steganography including PWRN  [ 35 ] .

Video Quality Assessment. Fig 4 shows qualitative results on the integrity of generated video frames. We perform tests within and across datasets, each containing 16 test samples. The generated faces effectively change individual identities while retaining attributes like expressions and poses. More findings are available in the supplementary materials. Fig 3 illustrates the visual effects of certain intermittent frames within the stego videos.

Comparisons on Extraction Accuracy & Robustness. We conduct extensive experiments with multiple types of distortions. Detailed distortion implementations are provided in the supplement.

The quantitative comparison results in terms of accuracy are reported in Table 1 . The results show that our method can successfully extract secret message with high accuracy even after severe distortions. LSB   [ 80 ] struggles even with PNG (quantization) and HiDDeN   [ 12 ] , though trained with a distortion module, can not generalize well to video-level distortions. PWRN   [ 35 ] demonstrates robustness across numerous distortions, yet its performance remains constrained under operations such as motion blur or contrast adjustment. The proposed RoGVS method shows superior robustness to these distortions while maintaining high extraction accuracy. Security Analysis. We use three video steganalysis tools to evaluate the security of our method. The detection performance of these three steganalysis schemes is presented in Table 4 . Table 4 demonstrates that our method exhibits slightly superior security compared to the three counterparts.

4.3 Ablation Study

Embedding Position of Secret Message. In our generation network with 9 Secret-ID blocks, we explore different positions for embedding the secret message. We divide the secret message into two 9-bit segments and allocate their positions. In detail, Setting (a): 1st-4th blocks and 5th-9th blocks. Setting (b): 1st-2nd blocks and 3rd-4th blocks. Setting (c): 5th-6th blocks and 7th-8th blocks. They are in comparison of the standard setting of RoGVS: 1st-3rd blocks and 4th-6th blocks.

Table 2 displays the performance for these four setups. Both Settings b and c show a considerable decrease compared to Settings a and d, suggesting that adding more Secret-ID blocks improves performance. Notably, Setting c outperforms Setting b, indicating the higher influence of subsequent blocks on the generated image.

Ablation on Attacking Layer, λ 𝜆 \lambda italic_λ & Discriminator. Fig 5 shows even without the module, our method demonstrates considerable robustness, surpassing the three comparative methods. The addition of attacking layer improves accuracy by an average of 6%.

Table 3 presents the impact of λ 𝜆 \lambda italic_λ on the extraction accuracy. More ablation results on λ 𝜆 \lambda italic_λ and the discriminator are displayed in the supplement.

5 Conclusions

We propose a robust generative video steganography method based on visual editing, which modifies semantic feature to embed secret message. We use face-swapping scenario as an example to show the effectiveness of our RoGVS. The results showcase that our method can generate high-quality visually edited stego videos. What’s more, RoGVS outperforms existing video and image steganography methods in robustness and capacity.

• [1] Authors, “The frobnicatable foo filter,” ACM MM 2013 submission ID 324. Supplied as additional material acmmm13.pdf .
• [2] Authors, “Frobnication tutorial,” 2012, Supplied as additional material tr.pdf .
• [3] J. W. Cooley and J. W. Tukey, “An algorithm for the machine computation of complex Fourier series,” Math. Comp. , vol. 19, pp. 297–301, Apr. 1965.
• [4] S. Haykin, “Adaptive filter theory,” Information and System Science. Prentice Hall, 4th edition, 2002.
• [5] Dennis R. Morgan, “Dos and don’ts of technical writing,” IEEE Potentials , vol. 24, no. 3, pp. 22–25, Aug. 2005.
• [6] Vojtěch Holub and Jessica Fridrich, “Designing steganographic distortion using directional filters,” in 2012 IEEE International workshop on information forensics and security (WIFS) . IEEE, 2012, pp. 234–239.
• [7] Vojtěch Holub, Jessica Fridrich, and Tomáš Denemark, “Universal distortion function for steganography in an arbitrary domain,” EURASIP Journal on Information Security , vol. 2014, pp. 1–13, 2014.
• [8] Bin Li, Ming Wang, Jiwu Huang, and Xiaolong Li, “A new cost function for spatial image steganography,” in 2014 IEEE International conference on image processing (ICIP) . IEEE, 2014, pp. 4206–4210.
• [9] Mahdi Ahmadi, Alireza Norouzi, Nader Karimi, Shadrokh Samavi, and Ali Emami, “Redmark: Framework for residual diffusion watermarking based on deep networks,” Expert Systems with Applications , vol. 146, pp. 113157, 2020.
• [10] Junpeng Jing, Xin Deng, Mai Xu, Jianyi Wang, and Zhenyu Guan, “Hinet: deep image hiding by invertible network,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 4733–4742.
• [11] Shao-Ping Lu, Rong Wang, Tao Zhong, and Paul L Rosin, “Large-capacity image steganography based on invertible neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 10816–10825.
• [12] Jiren Zhu, Russell Kaplan, Justin Johnson, and Li Fei-Fei, “Hidden: Hiding data with deep networks,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 657–672.
• [13] Andrew Brock, Jeff Donahue, and Karen Simonyan, “Large scale gan training for high fidelity natural image synthesis,” arXiv preprint arXiv:1809.11096 , 2018.
• [14] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” arXiv preprint arXiv:1710.10196 , 2017.
• [15] Tero Karras, Samuli Laine, and Timo Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4401–4410.
• [16] Ping Wei, Sheng Li, Xinpeng Zhang, Ge Luo, Zhenxing Qian, and Qing Zhou, “Generative steganography network,” in Proceedings of the 30th ACM International Conference on Multimedia , 2022, pp. 1621–1629.
• [17] Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena, “Self-attention generative adversarial networks,” in International conference on machine learning . PMLR, 2019, pp. 7354–7363.
• [18] Donghui Hu, Liang Wang, Wenjie Jiang, Shuli Zheng, and Bin Li, “A novel image steganography method via deep convolutional generative adversarial networks,” IEEE access , vol. 6, pp. 38303–38314, 2018.
• [19] Zhuo Zhang, Guangyuan Fu, Rongrong Ni, Jia Liu, and Xiaoyuan Yang, “A generative method for steganography by cover synthesis with auxiliary semantics,” Tsinghua Science and Technology , vol. 25, no. 4, pp. 516–527, 2020.
• [20] Zhuo Zhang, Jia Liu, Yan Ke, Yu Lei, Jun Li, Minqing Zhang, and Xiaoyuan Yang, “Generative steganography by sampling,” IEEE access , vol. 7, pp. 118586–118597, 2019.
• [21] Zhian Liu, Maomao Li, Yong Zhang, Cairong Wang, Qi Zhang, Jue Wang, and Yongwei Nie, “Fine-grained face swapping via regional gan inversion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 8578–8587.
• [22] Yi-Ting Cheng, Virginia Tzeng, Yu Liang, Chuan-Chang Wang, Bing-Yu Chen, Yung-Yu Chuang, and Ming Ouhyoung, “3d-model-based face replacement in video,” in SIGGRAPH’09: Posters , pp. 1–1. 2009.
• [23] Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Nießner, “Face2face: Real-time face capture and reenactment of rgb videos,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2387–2395.
• [24] Yuval Nirkin, Yosi Keller, and Tal Hassner, “Fsgan: Subject agnostic face swapping and reenactment,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 7184–7193.
• [25] Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, and Fang Wen, “Faceshifter: Towards high fidelity and occlusion aware face swapping,” arXiv preprint arXiv:1912.13457 , 2019.
• [26] Renwang Chen, Xuanhong Chen, Bingbing Ni, and Yanhao Ge, “Simswap: An efficient framework for high fidelity face swapping,” in Proceedings of the 28th ACM International Conference on Multimedia , 2020, pp. 2003–2011.
• [27] Zhili Zhou, Xiaohua Dong, Ruohan Meng, Meimin Wang, Hongyang Yan, Keping Yu, and Kim-Kwang Raymond Choo, “Generative steganography via auto-generation of semantic object contours,” IEEE Transactions on Information Forensics and Security , 2023.
• [28] Zichi Wang, Guorui Feng, Hanzhou Wu, and Xinpeng Zhang, “Data hiding during image processing using capsule networks,” Neurocomputing , vol. 537, pp. 49–60, 2023.
• [29] Shilpa Gupta, Geeta Gujral, and Neha Aggarwal, “Enhanced least significant bit algorithm for image steganography,” IJCEM International Journal of Computational Engineering & Management , vol. 15, no. 4, pp. 40–42, 2012.
• [30] Amritpal Singh and Harpal Singh, “An improved lsb based image steganography technique for rgb images,” in 2015 IEEE International Conference on electrical, computer and communication technologies (ICECCT) . IEEE, 2015, pp. 1–4.
• [31] Kevin Alex Zhang, Alfredo Cuesta-Infante, Lei Xu, and Kalyan Veeramachaneni, “Steganogan: High capacity image steganography with gans,” arXiv preprint arXiv:1901.03892 , 2019.
• [32] Ozdemir Cetin and A Turan Ozcerit, “A new steganography algorithm based on color histograms for data embedding into raw video streams,” computers & security , vol. 28, no. 7, pp. 670–682, 2009.
• [33] Yi Dong, Tanfeng Sun, and Xinghao Jiang, “A high capacity hevc steganographic algorithm using intra prediction modes in multi-sized prediction blocks,” in Digital Forensics and Watermarking: 17th International Workshop, IWDW 2018, Jeju Island, Korea, October 22-24, 2018, Proceedings 17 . Springer, 2019, pp. 233–247.
• [34] Jindou Liu, Zhaohong Li, Xinghao Jiang, and Zhenzhen Zhang, “A high-performance cnn-applied hevc steganography based on diamond-coded pu partition modes,” IEEE Transactions on Multimedia , vol. 24, pp. 2084–2097, 2021.
• [35] Zhonghao Li, Xinghao Jiang, Yi Dong, Laijin Meng, and Tanfeng Sun, “An anti-steganalysis hevc video steganography with high performance based on cnn and pu partition modes,” IEEE Transactions on Dependable and Secure Computing , vol. 20, no. 1, pp. 606–619, 2022.
• [36] Songhan He, Dawen Xu, Lin Yang, and Weipeng Liang, “Adaptive hevc video steganography with high performance based on attention-net and pu partition modes,” IEEE Transactions on Multimedia , 2023.
• [37] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4690–4699.
• [38] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al., “Spatial transformer networks,” Advances in neural information processing systems , vol. 28, 2015.
• [39] Nuur Alifah Roslan, Nur Izura Udzir, Ramlan Mahmod, and Adnan Gutub, “Systematic literature review and analysis for arabic text steganography method practically,” Egyptian Informatics Journal , 2022.
• [40] Nadia A Karim, Suhad A Ali, and Majid Jabbar Jawad, “A coverless image steganography based on robust image wavelet hashing,” TELKOMNIKA (Telecommunication Computing Electronics and Control) , vol. 20, no. 6, pp. 1317–1325, 2022.
• [41] Sheng Li, Zichi Wang, Xiudong Zhang, and Xinpeng Zhang, “Robust image steganography against general downsampling operations with lossless secret recovery,” IEEE Transactions on Dependable and Secure Computing , 2023.
• [42] Mahmoud M Mahmoud and Huwaida T Elshoush, “Enhancing lsb using binary message size encoding for high capacity, transparent and secure audio steganography–an innovative approach,” IEEE Access , vol. 10, pp. 29954–29971, 2022.
• [43] Omer Farooq Ahmed Adeeb and Seyed Jahanshah Kabudian, “Arabic text steganography based on deep learning methods,” IEEE Access , vol. 10, pp. 94403–94416, 2022.
• [44] M Hassan Shirali-Shahreza and Mohammad Shirali-Shahreza, “Text steganography in chat,” in 2007 3rd IEEE/IFIP International Conference in Central Asia on Internet . IEEE, 2007, pp. 1–5.
• [45] B Delina, “Information hiding: A new approach in text steganography,” in Proceedings of the International Conference on Applied Computer and Applied Computational Science, World Scientific and Engineering Academy and Society (WSEAS 2008) , 2008, pp. 689–695.
• [46] Fatiha Djebbar, Beghdad Ayad, Habib Hamam, and Karim Abed-Meraim, “A view on latest audio steganography techniques,” in 2011 International Conference on Innovations in Information Technology . IEEE, 2011, pp. 409–414.
• [47] Kaliappan Gopalan, “Audio steganography using bit modification,” in 2003 International Conference on Multimedia and Expo. ICME’03. Proceedings (Cat. No. 03TH8698) . IEEE, 2003, vol. 1, pp. I–629.
• [48] Fatiha Djebbar, Beghdad Ayad, Karim Abed Meraim, and Habib Hamam, “Comparative study of digital audio steganography techniques,” EURASIP Journal on Audio, Speech, and Music Processing , vol. 2012, no. 1, pp. 1–16, 2012.
• [49] Zhengxin You, Qichao Ying, Sheng Li, Zhenxing Qian, and Xinpeng Zhang, “Image generation network for covert transmission in online social network,” in Proceedings of the 30th ACM International Conference on Multimedia , 2022, pp. 2834–2842.
• [50] Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro, “High-resolution image synthesis and semantic manipulation with conditional gans,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8798–8807.
• [51] Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok, “Synthesizing robust adversarial examples,” in International conference on machine learning . PMLR, 2018, pp. 284–293.
• [52] Danying Hu, Daniel DeTone, and Tomasz Malisiewicz, “Deep charuco: Dark charuco marker pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8436–8444.
• [53] Richard Shin and Dawn Song, “Jpeg-resistant adversarial images,” in NIPS 2017 Workshop on Machine Learning and Computer Security , 2017, vol. 1, p. 8.
• [54] Matthew Tancik, Ben Mildenhall, and Ren Ng, “Stegastamp: Invisible hyperlinks in physical photographs,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2117–2126.
• [55] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778.
• [56] Xun Huang and Serge Belongie, “Arbitrary style transfer in real-time with adaptive instance normalization,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 1501–1510.
• [57] Ming-Yu Liu, Xun Huang, Arun Mallya, Tero Karras, Timo Aila, Jaakko Lehtinen, and Jan Kautz, “Few-shot unsupervised image-to-image translation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 10551–10560.
• [58] Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu, “Semantic image synthesis with spatially-adaptive normalization,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2337–2346.
• [59] Jonas Adler and Sebastian Lunz, “Banach wasserstein gan,” Advances in neural information processing systems , vol. 31, 2018.
• [60] Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville, “Improved training of wasserstein gans,” Advances in neural information processing systems , vol. 30, 2017.
• [61] Qiong Cao, Li Shen, Weidi Xie, Omkar M Parkhi, and Andrew Zisserman, “Vggface2: A dataset for recognising faces across pose and age,” in 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018) . IEEE, 2018, pp. 67–74.
• [62] Liming Zhai, Lina Wang, and Yanzhen Ren, “Universal detection of video steganography in multiple domains based on the consistency of motion vectors,” IEEE transactions on information forensics and security , vol. 15, pp. 1762–1777, 2019.
• [63] Zhonghao Li, Laijing Meng, Shutong Xu, Zhaohong Li, Yunqing Shi, and Yuanchang Liang, “A hevc video steganalysis algorithm based on pu partition modes.,” Computers, Materials & Continua , vol. 59, no. 2, 2019.
• [64] Q Sheng, RD Wang, ML Huang, Q Li, and D Xu, “A prediction mode steganalysis detection algorithm for hevc,” J Optoelectron-laser , vol. 28, no. 4, pp. 433–440, 2017.
• [65] Jiajun Huang, Xueyu Wang, Bo Du, Pei Du, and Chang Xu, “Deepfake mnist+: a deepfake facial animation dataset,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 1973–1982.
• [66] Xiyao Liu, Ziping Ma, Junxing Ma, Jian Zhang, Gerald Schaefer, and Hui Fang, “Image disentanglement autoencoder for steganography without embedding,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 2303–2312.
• [67] Robert Engelmore and Anthony Morgan, Eds., Blackboard Systems , Addison-Wesley, Reading, Mass., 1986.
• [68] William J. Clancey, “Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education,” in Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83) , Menlo Park, Calif, 1983, pp. 556–560, IJCAI Organization.
• [69] William J. Clancey, “Classification Problem Solving,” in Proceedings of the Fourth National Conference on Artificial Intelligence , Menlo Park, Calif., 1984, pp. 45–54, AAAI Press.
• [70] Arthur L. Robinson, “New ways to make microcircuits smaller,” Science , vol. 208, no. 4447, pp. 1019–1022, 1980.
• [71] Arthur L. Robinson, “New Ways to Make Microcircuits Smaller—Duplicate Entry,” Science , vol. 208, pp. 1019–1026, 1980.
• [72] Diane Warner Hasling, William J. Clancey, and Glenn Rennels, “Strategic explanations for a diagnostic consultation system,” International Journal of Man-Machine Studies , vol. 20, no. 1, pp. 3–19, 1984.
• [73] Diane Warner Hasling, William J. Clancey, Glenn R. Rennels, and Thomas Test, “Strategic Explanations in Consultation—Duplicate,” The International Journal of Man-Machine Studies , vol. 20, no. 1, pp. 3–19, 1983.
• [74] James Rice, “Poligon: A System for Parallel Problem Solving,” Technical Report KSL-86-19, Dept. of Computer Science, Stanford Univ., 1986.
• [75] William J. Clancey, Transfer of Rule-Based Expertise through a Tutorial Dialogue , Ph.D. diss., Dept. of Computer Science, Stanford Univ., Stanford, Calif., 1979.
• [76] William J. Clancey, “The Engineering of Qualitative Models,” Forthcoming, 2021.
• [77] Mathieu Bouville, “Crime and punishment in scientific research,” 2008.
• [78] NASA, “Pluto: The ’other’ red planet,” \url https://www.nasa.gov/nh/pluto-the-other-red-planet, 2015, Accessed: 2018-12-06.
• [79] Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner, “Faceforensics++: Learning to detect manipulated facial images,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 1–11.
• [80] Chi-Kwong Chan and Lee-Ming Cheng, “Hiding data in images by simple lsb substitution,” Pattern recognition , vol. 37, no. 3, pp. 469–474, 2004.
• [81] Yu Wang, Yun Cao, Xianfeng Zhao, Zhoujun Xu, and Meineng Zhu, “Maintaining rate-distortion optimization for ipm-based video steganography by constructing isolated channels in hevc,” in Proceedings of the 6th ACM workshop on information hiding and multimedia security , 2018, pp. 97–107.
• [82] Haiwei Wu, Jiantao Zhou, Jinyu Tian, and Jun Liu, “Robust image forgery detection over online social network shared images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 13440–13449.
• [83] Shichao Dong, Jin Wang, Renhe Ji, and et al., “Implicit identity leakage: The stumbling block to improving deepfake detection generalization,” in CVPR , 2023.
• [84] Wenliang Zhao, Yongming Rao, Weikang Shi, and et al., “Diffswap: High-fidelity and controllable face swapping via 3d-aware masked diffusion,” in CVPR , 2023.
• [85] Zhendong Wang, Jianmin Bao, and et al., “Altfreezing for more general video face forgery detection,” in CVPR , 2023.

Universal Detection of Video Steganography in Multiple Domains Based on the Consistency of Motion Vectors

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A review of steganography techniques

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Ali Mahmood Khalaf , Kamaljit Lakhtaria; A review of steganography techniques. AIP Conf. Proc. 16 February 2024; 3051 (1): 040005. https://doi.org/10.1063/5.0191705

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Due to the importance, security, confidentiality, privacy, and progress of information at present due to the development in the field of information technology, sharing this information (text, image, audio, video) has become a critical problem due to the exposure of this information to penetration by attacking parties, and to solve the problem of penetration, many technologies have appeared to protect This information (text, image, audio, video) is cryptography algorithms and information steganography techniques. Steganography is a science that maintains the confidentiality of information (text, image, audio, video) between two communicating parties (the sender and the receiver), and it is a science that studies the invisible communication of information. In this paper, the researcher reviewed the most important techniques of previous studies for the last six years, such as LSB, PVD, DFT, DWT, DCT, Masking & Filtering, Distortion, and Statistical. In the science of hiding information, and analyzing it in terms of technical domain, invisibility, capacity, and complexity, all of these techniques can hide this information from hostile attacks. In this paper, the evaluation criteria for hiding the information (text, image, audio, video) were reviewed in terms of imperceptibility, robustness, capacity, and security. Also, the most important applications of hiding techniques (text, image, audio, video) were reviewed in this paper.

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Here are 21 public repositories matching this topic..., zhanghong863 / feature-extractors-for-video-steganalysis.

To provide the stego community with C/C++ implementations of selected feature extractors mainly targeted at H.264 steganography.

• Updated Jun 2, 2021

Priyansh-15 / Steganography-Tools

A project named "STEGANOGRAPHY TOOLS " that provide 4 types of Steganography { Image, Text, Audio, Video } that hides User's Text message in the desired cover file using the tool and can send it to the receiver who can extract the Hidden message using the same tool .

• Updated Nov 25, 2023

anilsathyan7 / Deep-Video-Steganography-Hiding-Videos-in-Plain-Sight

• Updated Nov 20, 2020

itxKAE / Video-Steganography

School Assignment

• Updated Oct 31, 2020

Akshay-Arjun / Video-Steganography

AES 256 & RSA encrypted video steganography. SRU Hackathon 2022 - Cybersecurity Winners

• Updated Jan 6, 2024

Sanjipan / Steganography

A Advance Steganography Tool made with Python The Program Supports Hiding Secret Text data into an innocent looking cover file like : .mp4, .wav, .png, .jpeg, .txt through the use of Steganography Theory.

• Updated Feb 25, 2023

Kunal-Attri / Data-Security-using-Cryptography-and-Steganography

Improved way of securing data using combined Steganography and Cryptographic techniques

• Updated Jul 25, 2023

petermcneil / lodge

A video steganography tool; combining video files and subtitles

• Updated Jul 26, 2021

mightymoogle / StegoVideoDemo

Video Steganography / Watermarking Demo Tool

• Updated Jan 16, 2022

williamprout / NetworkVideoSteganography

Program that uses steganography and cryptography to hide a video file within another video file without noticeably effecting the appearance of the video. Networking component to securely send encoded files over a network.

• Updated Apr 6, 2022

Abanteeka / Steganography

A Advance Steganography Tool made with Python The Program Supports Hiding Secret Text data into an innocent looking cover file like : .mp4, .wav, .png, .jpeg, .txt through the use of Steganography Theory

koustubh1317 / Steganography-Tools

This project "STEGANOGRAPHY TOOLS" showcases algorithms to hide text message in four different cover file (Text, Image, Audio, Video) and transfer it to the receiver who can extract the hidden message from these files with the same tools.

• Updated Oct 27, 2022
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Prince326 / VideoSteganography-using-python3

• Updated Apr 9, 2020

DLarisa / Dissertation

Dissertation for master of Security and Applied Logic at FMI, UniBuc (Video Steganography - Topic)

• Updated Jul 11, 2023

olaitanade / stegaspy

Video steganography project for windows in wpf.

• Updated Dec 2, 2019

hrichharms / dct_coefficients_analysis

Explores both the relationships between discrete cosine transform (DCT) coefficients as well as the variation in DCT coefficients across popular video codecs with a focus on improving and optimizing DCT-based steganographic systems.

• Updated Jul 29, 2022

shrutityagi4102 / Information-Security-IA1

Video Steganography in Python for our Information Security Internal Assessment 1

• Updated Feb 23, 2023

HeeyaAmin / Steganography_Vid

I am thrilled to delve into the captivating realm of video steganography, driven by a fervent interest in concealing secret image data within cover videos. My project involves a meticulous process of selecting specific frames and applying the tried-and-true LSB techniques to seamlessly embed information.

• Updated Jun 9, 2023

createunique / STEGANOGRAPHY_HIDDEN_HARBOR

Explore a versatile Python repository enabling seamless steganography across Text, Image, Audio, GIF, and Video formats.

• Updated Apr 24, 2024

nishant-kumarr / Hidden_Layer

• Updated May 4, 2024

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A survey on information hiding using video steganography

• Published: 09 February 2021
• Volume 54 , pages 5831–5895, ( 2021 )

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• Mukesh Dalal   ORCID: orcid.org/0000-0002-3473-6652 1 &
• Mamta Juneja 1  

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In the last few decades, information security has gained huge importance owing to the massive growth in digital communication; hence, driving steganography to the forefront for secure communication. Steganography is a practice of concealing information or message in covert communication which involves hiding the information in any multimedia file such as text, image, or video. Many contributions have been made in the domain of image steganography; however, due to the low embedding capacity and robustness of images; videos are gaining more attention of academic researchers. This paper aims to provide a qualitative as well as quantitative analysis of various video steganography techniques by highlighting their properties, challenges, pros, and cons. Moreover, different quality metrics for the evaluation of distinct steganography techniques have also been discussed. The paper also provides an overview of steganalysis attacks which are commonly employed to test the security of the steganography techniques. The experimental analysis of some of the prominent techniques using different quality metrics has also been done. This paper also presented a critical analysis driven from the literature and the experimental results. The primary objective of this paper is to help the beginners to understand the basic concepts of this research domain to initiate their research in this field. Further, the paper highlighted the real-life applications of video steganography and also suggested some future directions which require the attention of the research community.

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Abbass AS, Soleit EA, Ghoniemy SA (2007) Blind video data hiding using integer wavelet transforms. Ubiquitous Comput Commun J 2(1):11–25

Google Scholar  

Abdolmohammadi M, Toroghi RM, Bastanfard A (2019) Video steganography using 3D convolutional neural networks. Paper presented at Mediterranean conference on pattern recognition and artificial intelligence. pp. 149–161

Abu-El-Haija S, Kothari N, Lee J, Natsev P, Toderici G, Varadarajan B, Vijayanarasimhan S (2016) Youtube-8m: a large-scale video classification benchmark. arXiv Prepr. arXiv1609.08675

Ahmed EAE, Soliman HH, Mostafa HE (2014) Information hiding in video files using frequency domain. Int J Sci Res 3(6):2431–2437

Al-Frajat AK, Jalab HA, Kasirun ZM, Zaidan AA, Zaidan BB (2010) Hiding data in video file: an overview. J Appl Sci 10:1644–1649

Article   Google Scholar  

Aljahdali H, Townend P, Xu J (2013) Enhancing multi-tenancy security in the cloud IaaS model over public deployment. Paper presented at IEEE 7th International Symposium on in Service Oriented System Engineering (SOSE), 385–390

Aly HA (2011) Data hiding in motion vectors of compressed video based on their associated prediction error. IEEE Trans Inf Forensics Secur 6(1):14–18

Amirtharajan R, Rayappan JBB (2013) Steganography-time to time: a review. Res J Inform Technol 5:53–66

Asikuzzaman M, Pickering MR (2018) An overview of digital video watermarking. IEEE Trans Circuits Syst Video Technol 28(9):2131–2153

Babaguchi N, Cavallaro A, Chellappa R, Dufaux F, Wang L (2013) Guest editorial: special issue on intelligent video surveillance for public security and personal privacy. IEEE Trans Inf Forensics Secur 8(10):1559–1561

Balaji R, Naveen G (2011) Secure data transmission using video Steganography. Paper presented at IEEE International Conference in Electro/Information Technology (EIT). pp. 1–5

Balu S, Babu CNK, Amudha K (2018) Secure and efficient data transmission by video steganography in medical imaging system. Cluster Comput. 22(2):1–7

Bancroft FC, Clelland C (2001) DNA-based steganography. Google Patents, 2001

Bandyopadhyay SK, Bhattacharyya D, Ganguly D, Mukherjee S, Das P (2008) A tutorial review on steganography. Int Conf Contemp Comput 101:105–114

Bhattacharyya D, Bhaumik AK, Choi M, Kim TH (2010) Directed graph pattern synthesis in LSB technique on video steganography. Lect Notes Comput Sci 6059(1996):61–69

Bhautmage P, Jeyakumar A, Dahatonde A (2013) Advanced video steganography algorithm. Int J Eng Res Appl 3:1641–1644

Bo P, Jie Y (2018) A Reversible information hiding method based on HEVC. IFAC-PapersOnLine 51(17):238–243

Braun H, Lamothe E, Letort D (2018) Introduction: Les cultures ado, consommation et production. Publije, no. 1

Cao Y, Zhao X, Feng D, Sheng R (2011) Video steganography with perturbed motion estimation. In International workshop on information hiding. Springer, Berlin, Heidelberg, pp. 193–207

Cao Y, Zhao X, Feng D (2012) Video steganalysis exploiting motion vector reversion-based features. IEEE Signal Process Lett 19(1):35–38

Cao Y, Zhang H, Zhao X, Yu H (2015) Covert communication by compressed videos exploiting the uncertainty of motion estimation. IEEE Commun Lett 19(2):203–206

Cetin O, Ozcerit AT (2009) A new steganography algorithm based on color histograms for data embedding into raw video streams. Comput Secur 28(7):670–682

Chae JJ, Manjunath BS (1999) Data hiding in video. In: Proceedings of the 1999 International Conference on image processing. Vol. 1, pp. 311–315

Chandramouli R (2003) Steganography capacity: a steganalysis perspective. Proc SPIE. pp. 173–177

Chantrapornchai C, Churin K, Preechasuk J, Adulkasem S (2014) Video Steganography For Hiding Image With Wavelet Coefficients. Int J Multimed Ubiquitous Eng 9(6):385–396

Cheddad A, Condell J, Curran K, Mc Kevitt P (2010) Digital image steganography: survey and analysis of current methods. Signal Process 90(3):727–752

Article   MATH   Google Scholar  

Chen S, Qu Z (2018) Novel quantum video steganography and authentication protocol with large payload. Int J Theor Phys 57(12):3689–3701

Choubey S, Bansal A (2014) Video steganography using neural network methods. Int J Res Advent Technol 2(2)

Cimpanu C (2016) Steganography is very popular with exploit kits all of a sudden [Online]. https://www.bleepingcomputer.com/news/security/steganography-is-very-popular-with-exploit-kits-all-of-a-sudden/ . Accessed 13 Dec 2017

Dalal M, Juneja M (2016) Overview of video steganography in compressed domain. Int J Comput Technol Appl 9(10):1–11

Dalal M, Juneja M (2018) Video steganalysis to obstruct criminal activities for digital forensics: a survey. Int J Electron Secur Digit Forensics 10(4):338–355

Dalal M, Juneja M (2018) Video steganography techniques in spatial domain-a survey. In: Proceedings of the international conference on computing and communication systems, Springer, Singapore. pp. 705–711

Dalal M, Juneja M (2019) A robust and imperceptible steganography technique for SD and HD videos. Multimed Tools Appl 78(5):5769–5789

Dalal M, Juneja M (2020) Evaluation of orthogonal and biorthogonal wavelets for video steganography. Inf Secur J A Glob Perspect 29(1):1–11

Darmstaedter V, Delaigle J-F, Quisquater J-J, Macq B (1998) Low cost spatial watermarking. Comput Graph 22(4):417–424

Dasgupta K, Mandal JK, Dutta P (2012) Hash based least significant bit technique for video steganography (HLSB). Int J Secur Priv Trust Manag 1(2):1–11

Dasgupta K, Mondal JK, Dutta P (2013) Optimized video steganography using Genetic Algorithm (GA). Procedia Technol 10:131–137

Easttom C (2017) System forensics, investigation, and response. Jones & Bartlett Learning, Burlington

Engineer arrested for attempted theft of trade secrets, Dark Reading (2017) [Online]. https://www.darkreading.com/vulnerabilities---threats/engineer-arrested-for-attempted-theft-of-trade-secrets/d/d-id/1328635 . Accessed 03 Feb 2019

Esen E, Alatan AA (2011) Robust video data hiding using forbidden zone data hiding and selective embedding. IEEE Trans Circuits Syst Video Technol 21(8):1130–1138

Fan M, Liu P, Wang H, Sun X (2016) Cross correlation feature mining for steganalysis of hash based least significant bit substitution video steganography. Telecommun Syst. 63:1–7

Faragallah OS (2013) Efficient video watermarking based on singular value decomposition in the discrete wavelet transform domain. AEU Int J Electron Commun 67(3):189–196

Ferreira AM (2015) An overview on hiding and detecting stego-data in video streams. [Online]. https://homepages.staff.os3.nl/~delaat/rp/2014-2015/p66/report.pdf . Accessed 12 Sep 2016

Firmansyah DM and, Ahmad T (2016) An improved neighbouring similarity method for video steganography. Paper presented at 4th international conference on cyber and IT service management, pp. 1–5

Forczmański P, Wekegrzyn M (2008) Virtual steganographic laboratory for digital images. Inf Syst Archit Technol Inf Syst Comput Commun Networks Wrocław Pol. pp. 163–174

Fridrich J, Goljan M (2002) Practical steganalysis of digital images: state of the art. Electronic Imaging 2002:1–13

Fridrich J, Goljan M, Soukal D (2003) Higher-order statistical steganalysis of palette images. Proc SPIE 5020:178–190

Fu P-W, Wu C-C, Cho Y-J (2017) What makes users share content on facebook? Compatibility among psychological incentive, social capital focus, and content type. Comput Human Behav 67:23–32

Furuta T, Noda H, Niimi M, Kawaguchi E (2003) Bit-plane decomposition steganography using wavelet compressed video. In: proceedings of the 2003 joint conference of the fourth international conference on information, communications and signal processing, 2003 and fourth pacific Rim conference on multimedia. Vol. 2. pp. 970–974

Galiano DR, Del Barrio AA, Botella G, Cuesta D (2020) Efficient embedding and retrieval of information for high-resolution videos coded with HEVC. Comput Electr Eng 81:106541

Gallagher S (2012) Steganography: how al-Qaeda hid secret documents in a porn video. [Online]. https://arstechnica.com/business/2012/05/steganography-how-al-qaeda-hid-secret-documents-in-a-porn-video/ . Accessed 01 Jul 2016

Gross MJ (2011) Exclusive: operation shady RAT: unprecedented cyber-espionage campaign and intellectual-property bonanza. Vanity Fair, vol. 2

Gujjunoori S, Amberker BB (2013) DCT based reversible data embedding for MPEG-4 video using HVS characteristics. J Inf Secur Appl 18(4):157–166

Gupta H, Chaturvedi DS (2013) Video data hiding through LSB substitution technique. Res Inven Int J Eng Sci 2(10):32–39

Gupta H, Chaturvedi S (2014) Video steganography through LSB based hybrid approach. Int J Comput Sci Netw Secur 14(3):99–106

H.264/AVC. [Online]. https://en.wikipedia.org/wiki/H.264/MPEG-4_AVC . Accessed 21 Nov 2016

Hanafy AA, Salama GI, Mohasseb YZ (2005) A secure covert communication model based on video steganography. In: Proceedings of the IEEE MILCOM Military Communication Conference. pp. 1–6, 2008

Hashemzadeh M (2018) Hiding information in videos using motion clues of feature points. Comput Electr Eng 68:14–25

He Y, Yang G, Zhu N (2012) A real-time dual watermarking algorithm of H. 264/AVC video stream for video-on-demand service. AEU International J Electron Commun 66(4):305–312

Holmes B (2015) Steganography: how Antonopoulos hid a US$100m transaction in a picture of kittens. [Online]. https://bravenewcoin.com/news/steganography-how-antonopoulos-hid-a-us12m-transaction-in-a-picture-of-kittens/ . Accessed 13 Dec 2017

Hu, SD KTU, “A novel video steganography based on non-uniform rectangular partition,” Proc. - 14th IEEE Int. Conf. Comput. Sci. Eng. CSE 2011 11th Int. Symp. Pervasive Syst. Algorithms, Networks, I-SPA 2011 10th IEEE Int. Conf. IUCC 2011 , pp. 57–61, 2011.

Hussain M, Wahab AWA, Bin Idris YI, Ho ATS, Jung K-H (2018) Image steganography in spatial domain: a survey. Signal Process Image Commun 65:46–66

Idbeaa T, Samad SA, Husain H (2016) A secure and robust compressed domain video steganography for intra-and inter-frames using embedding-based byte differencing (EBBD) scheme. PLoS ONE 11(3):e0150732

Jiang B, Yang G, Chen W (2015) A CABAC based HEVC video steganography algorithm without bitrate increase. J Comput Inf Syst 11(6):2121–2130

Johnson NF, Jajodia S (1998) Exploring steganography: seeing the unseen. IEEE Comput 31(2):26–34

Johnson NF, Duric Z, Jajodia S (2001) Information hiding: steganography and watermarking-attacks and countermeasures: steganography and watermarking: attacks and countermeasures. Springer, Berlin

Book   Google Scholar  

Jue W, Min-qing Z, S. Juan-li S (2011) Video steganography using motion vector components. In: proceedings of the IEEE 3rd international conference on communication software and networks (ICCSN), pp. 500–503

Kamil S, Ayob M, Abdullah SNHS, Ahmad Z (2018) Optimized data hiding in complemented or non-complemented form in video steganography. Cyber Resilience Conf 2018:1–4

Kapoor V, Mirza A (2015) An enhanced LSB based video steganographic system for secure and efficient data transmission. Int J Comput Appl 121(10):38–42

Kaur R (2016) Kaur S (2016) XOR-EDGE based video steganography and testing against Chi-square steganalysis. Int J Image Graph Signal Process 8(9):31–39

Kaur M, Kaur A (2014) Improved security mechanism of text in video using steganographic technique. Int J Adv Res Comput Sci Softw Eng. vol. 2, no. 10

Kaur K, Kaur B (2018) DWT-LSB approach for video steganography using artificial neural network. Int. Adv. Res. J. Sci. Eng. Technol, IARJSET

Kelash HM, Wahab OFA, Elshakankiry OA, El-sayed HS (2014) Utilization of steganographic techniques in video sequences. Int J Comput Netw Technol 24(1):17–24

Kelley J (2001) Terrorist instructions hidden online, [Online]. http://usatoday30.usatoday.com/tech/news/2001-02-05-binladen-side.htm . Accessed 05 Jul 2016

Ker T, Andrew D, Bas, Bohme P, Cogranne R, Craver R, Filler S, Fridrich T, Pevny J (2013) Moving steganography and steganalysis from the laboratory into the real world. Proceedings of the first ACM workshop on Information hiding and multimedia security. pp. 45–58

Kessler GC (2004) Steganography: implications for the prosecutor and computer forensics examiner. American Prosecution Reasearch Institute

Khare R, Mishra R, Arya I (2014) Video steganography using LSB technique by neural network. Int Conf Comput Intell Commun Netw 2014:898–902

Khosla S, Kaur P (2014) Secure data hiding technique using video steganography and watermarking. Int J Comput Appl 95(20):7–12

Kolakalur A, Kagalidis I, Vuksanovic B, Iacsit M (2016) Wavelet based color video steganography. Int J Eng Technol 8(3):165

Konyar MZ, Akbulut O, Öztürk S (2020) Matrix encoding-based high-capacity and high-fidelity reversible data hiding in HEVC. Signal Image Video Process 14:1–9

Kopeytsev V (2020) Steganography in attacks on industrial enterprises, Kaspersky ICS CERT, [Online]. https://ics-cert.kaspersky.com/reports/2020/06/17/steganography-in-attacks-on-industrial-enterprises/ . Accessed 07 Jul 2020

Krenn R (2004) Steganography and steganalysis. Retrieved Sept 8:1–9

Kulkarni A, Goldman J, Nabholz B, Eyre W (2009) Detection of steganography-producing software artifacts on crime-related seized computers. J Digit Forensics Secur Law 4(2):5–26

Kumar P, Singh K (2018) An improved data-hiding approach using skin-tone detection for video steganography. Multimed Tools Appl 77(18):24247–24268

Le Gall D (1991) MPEG: a video compression standard for multimedia applications. Commun ACM 34(4):46–58

Lie W-N, Lin T-I, Lin C-W (2006) Enhancing video error resilience by using data-embedding techniques. IEEE Trans Circuits Syst Video Technol 16(2):300–308

Lin TJ, Chung KL, Chang PC, Huang YH, Liao HYM, Fang CY (2013) An improved DCT-based perturbation scheme for high capacity data hiding in H.264/AVC intra frames. J Syst Softw 86(3):604–614

Liu S, Xu D (2020) A robust steganography method for HEVC based on secret sharing. Cogn Syst Res 59:207–220

Liu B, Liu F, Lu B, Luo X (2006) Real-time steganography in compressed video. Paper presented at international workshop on multimedia content representation, classification and security, pp. 43–48

Liu B, Liu F, Ni D (2006) Adaptive compressed video steganography in the VLC-domain. Paper presented at 2006 IET international conference on wireless, mobile and multimedia networks. pp. 1–4

Liu B, Liu F, Yang C, Sun Y (2008) Secure steganography in compressed video bitstreams. Paper presented at 3rd international conference on ARES Availability, Reliability and Security. pp. 1382–1387

Liu Y, Hu M, Ma X, Zhao H (2015) A new robust data hiding method for H. 264/AVC without intra-frame distortion drift. Neurocomputing 151:1076–1085

Liu Y, Ju L, Hu M, Zhao H, Jia S, Jia Z (2016) A new data hiding method for H.264 based on secret sharing. Neurocomputing 188:113–119

Liu Y, Liu S, Zhao H, Liu S (2019) A new data hiding method for H. 265/HEVC video streams without intra-frame distortion drift. Multimed Tools Appl 78(6):6459–6486

Lu Y, Lu C, Qi M (2010) An effective video steganography method for biometric identification. Lect Notes Comput Sci 6059:469–479

Luo W, Huang F, Huang J (2010) Edge adaptive image steganography based on LSB matching revisited. IEEE Trans Inf forensics Secur 5(2):201–214

Luo T, Jiang G, Yu M, Xu H (2016) Asymmetric self-recovery oriented stereo image watermarking method for three dimensional video system. Multimed Syst 22(5):641–655

Luo T, Jiang G, Yu M, Xu H, Gao W (2017) Sparse recovery based reversible data hiding method using the human visual system. Multimed Tools Appl 77(15):1–24

Luo T, Zuo L, Jiang G, Gao W, Xu H, Jiang Q (2020) Security of MVD-based 3D video in 3D-HEVC using data hiding and encryption. J Real-Time Image Process 17(4):773–785

Ma X, Li Z, Tu H, Zhang B (2010) A data hiding algorithm for h.264/AVC video streams without intra-frame distortion drift. IEEE Trans Circuits Syst Video Technol 20(10):1320–1330

Mandal JK (2016) Handbook of research on natural computing for optimization problems. IGI Global, Pennsylvania

Manikandan G, Bala Krishnan R, Rajesh Kumar N, Sairam N, Raajan NR (2017) A steganographic approach for realizing medical data privacy in a distributed environment. Biomed Res 28(3)

Manisha S, Sharmila TS (2019) A two-level secure data hiding algorithm for video steganography. Multidimens Syst Signal Process 30(2):529–542

Mazurczyk W, Caviglione L (2015) Information hiding as a challenge for malware detection. arXiv Prepr. arXiv1504.04867

Mazurczyk W, Wendzel S, Zander S, Houmansadr A, Szczypiorski K (2016) Information hiding in communication networks: fundamentals, mechanisms, applications, and countermeasures, vol 7. Wiley, New Jersey

Mielikainen J (2006) LSB matching revisited. IEEE Signal Process Lett 13(5):285–287

Mollin RA (2000) An introduction to cryptography. CRC Press, Florida

MATH   Google Scholar  

Moon SK, Raut RD (2013) “Analysis of secured video steganography using computer forensics technique for enhance data security”, 2013 IEEE 2nd Int . Conf Image Inf Process IEEE ICIIP 2013:660–665

Mstafa RJ, Elleithy KM (2014) A highly secure video steganography using Hamming code (7, 4). IEEE Long Isl Syst Appl Technol Conf LISAT 2014:2014

Mstafa RJ, Elleithy KM (2015a) “A high payload video steganography algorithm in DWT domain based on BCH codes (15, 11)”, in. Wireless Telecommun Symp 2015:1–8

Mstafa RJ, Elleithy KM (2015) A new video steganography algorithm based on the multiple object tracking and Hamming codes. Paper presented at IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 335–340

Mstafa RJ, Elleithy KM (2016) A video steganography algorithm based on Kanade-Lucas-Tomasi tracking algorithm and error correcting codes. Multimed Tools Appl 75(17):10311–10333

Mstafa RJ, Elleithy KM (2016a) An ECC/DCT-based robust video steganography algorithm for secure data communication. J Cyber Secur Mobil 5(3):167–194

Mstafa RJ, Elleithy KM (2017) Compressed and raw video steganography techniques: a comprehensive survey and analysis. Multimed Tools Appl 76(20):21749–21786

Mstafa RJ, K. M. Elleithy KM (2016) A DCT-based robust video steganographic method using BCH error correcting codes. Paper presented at 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT), pp. 1–6

Mstafa RJ, Elleithy KM, Abdelfattah E (2017a) A robust and secure video steganography method in DWT-DCT domains based on multiple object tracking and ECC. IEEE Access 5:5354–5365

Mstafa RJ, Elleithy KM, Abdelfattah E (2017) Video steganography techniques: taxonomy, challenges, and future directions. Paper presented at systems, applications and technology conference (LISAT) IEEE Long Island. pp. 1–6

Mumthas S, Lijiya A (2017) Transform domain video steganography using RSA, random DNA encryption and Huffman encoding. Procedia Comput Sci 115:660–666

Mstafa RJ, Ellleithy KM (2015) An efficient video steganography algorithm based on BCH codes. Paper presented at Northeast Sect Conf Am Soc Eng Educ, no. May 2015

Narayanan KL, Prabakaran G, Bhavani R (2012) A high capacity video steganography based on integer wavelet transform. J Comput Appl 5(EICA2012-4):358–365

Neuner S, Voyiatzis AG, Schmiedecker M, Brunthaler S, Katzenbeisser S, Weippl ER (2016) Time is on my side: steganography in filesystem metadata. Digit Investig 18:S76–S86

Newman LH (2018) Mysterious ‘MuslimCrypt’ app helps jihadists send covert messages, Wired, [Online]. https://www.wired.com/story/muslimcrypt-steganography/ . Accessed 03 Feb 2019

News B (2016) FBI allegations against ‘Russian spies’ in US. [Online]. http://www.bbc.com/news/10442869 . Accessed 16 Aug 2016

Nichols S (2019) A picture tells a 1,000 words. Pixels pwn up to 5 million nerds: Crims use steganography to stash bad code in ads, The Register [Online]. https://www.theregister.co.uk/2019/01/24/mac_steganography_malware/ . Accessed 03 May 2020

Nie Q, Weng J, Xu X, Feng B (2018) Defining embedding distortion for intra prediction mode-based video steganography. Comput Mater Contin 55:59–70

Noda H, Furuta T, Niimi M, Kawaguchi E (2004) Application of BPCS steganography to wavelet compressed video. In proceedings of the International conference on image processing. ICIP. Vol. 4, no. 1, pp. 2147–2150

Ohm J-R, Sullivan GJ, Schwarz H, Tan TK, Wiegand T (2012) Comparison of the coding efficiency of video coding standards-including high efficiency video coding (HEVC). IEEE Trans circuits Syst video Technol 22(12):1669–1684

Ostermann J, Bormans J, List P, Marpe D, Narroschke M, Pereira F, Stockhammer T, Wedi T (2004) Video coding with H. 264/AVC: tools, performance, and complexity. IEEE Circuits Syst Mag 4(1):7–28

Pan F, Xiang L, Yang X, Guo Y (2010) Video steganography using motion vector and linear block codes. Paper presented at IEEE international conference on software engineering and service sciences. No. 60842006, pp. 592–595

Papadopoulos NA, Psannis KE (2018) Sequential Multiple LSB methods and real-time data hiding: variations for Visual Cryptography ciphers. J Real-Time Image Process 14(1):75–86

Patel K, Rora KK, Singh K, Verma S (2013) Lazy wavelet transform based steganography in video. In: Proceedings of 2013 International Conference on Communication Systems and Network Technologies (CSNT). pp. 497–500

Paul R, Acharya AK, Yadav VK, Batham S (2013) Hiding large amount of data using a new approach of video steganography,” in Confluence 2013: The Next Generation Information Technology Summit (4th International Conference), 2013, pp. 337–343.

Perumal K, Muthusamy S, Gengavel G (2018) Sparse data encoder and decoder to improve security in video steganography. Concurr Comput Pract Exp 31:1–7

Petitcolas FAP, Anderson RJ, Kuhn MG (1999) Information hiding-a survey. Proc IEEE 87(7):1062–1078

Pevny T, Fridrich J (2008) Benchmarking for steganography. Paper presented at international workshop on information hiding. pp. 251–267

Pfitzmann B (1996) Information hiding terminology-results of an informal plenary meeting and additional proposals. In: Proceedings of the first international workshop on information hiding. pp. 347–350

Pilania U, Gupta P (2020) “Analysis and implementation of IWT-SVD scheme for video steganography”, in micro-electronics and telecommunication engineering. Springer, Singapore

Pinson MH, Wolf S (2004) A new standardized method for objectively measuring video quality. IEEE Trans Broadcast 50(3):312–322

Qu Z, Chen S, Ji S (2017) A novel quantum video steganography protocol with large payload based on MCQI quantum video. Int J Theor Phys 56(11):3543–3561

Rabie T, Baziyad M (2019) The Pixogram: addressing high payload demands for video steganography. IEEE Access 7:21948–21962

Raggo MT, Hosmer C (2012) Data hiding: exposing concealed data in multimedia, operating systems, mobile devices and network protocols. Newnes, Oxford

Rajalakshmi K, Mahesh K (2018) ZLBM: zero level binary mapping technique for video security. Multimed Tools Appl 77(11):13225–13247

Ramalingam M (2011) Stego machine–video steganography using modified LSB algorithm. World Acad Sci Eng Technol 74:502–505

Ramalingam M, Isa NAM (2014) Video steganography based on integer Haar wavelet transforms for secured data transfer. Indian J Sci Technol 7(7):897–904

Ramalingam M, Isa NAM (2015) A steganography approach over video images to improve security. Indian J Sci Technol 8(1):79–86

Ramalingam M, Isa NAM (2016) A data-hiding technique using scene-change detection for video steganography. Comput Electr Eng 54:423–434

Ramalingam M, Ashidi N, Isa M (2015) Fast retrieval of hidden data using enhanced hidden Markov model in video steganography. Appl Soft Comput J 34:744–757

Rashmi N, Jyothi K (2018) An improved method for reversible data hiding steganography combined with cryptography. In: Proceedings of the 2nd international conference on inventive systems and control (ICISC), pp. 81–84

Reisslein M (2012) Video trace library. Arizona State University. http//trace. eas. asu. edu. Accessed 2012

Ren Y, Zhai L, Wang L, Zhu T (2014) Video steganalysis based on subtractive probability of optimal matching feature. In: Proceedings of the 2nd ACM workshop on Information hiding and multimedia security. pp. 83–90

Rezagholipour K, Eshghi M (2016) Video steganography algorithm based on motion vector of moving object. Paper presented at eighth international conference on information and knowledge technology (IKT), pp. 183–187

Richardson IE (2004) H.264 and MPEG-4 video compression: video coding for next-generation multimedia. John Wiley & Sons, New Jersey

Robie DL, Mersereau RM (2001) Video error correction using steganography. In: Proceedings of the International Conference on Image Processing (Cat. No.01CH37205), vol. 1, pp. 164–173

Roque JJ, Minguet JM (2009) SLSB: improving the Steganographic Algorithm LSB. In WOSIS, 2009, pp. 57–66

Rowland CH (1997) Covert channels in the TCP/IP protocol suite. First Monday, vol. 2, no. 5

“S-tools.” [Online]. Available: http://www.jjtc.com/stegdoc/sec316.html . Accessed: 26 Aug 2016

Sabeti V, Samavi S, Mahdavi M, Shirani S (2007) Steganalysis of pixel-value differencing steganographic method. Paper presented at IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, pp. 292–295

Sadat ES, Faez K, Saffari Pour M (2018) Entropy-based video steganalysis of motion vectors. Entropy 20(4):244–257

Sadek MM, Khalifa AS, Mostafa MGM (2015) Video steganography: a comprehensive review. Multimed Tools Appl 74(17):7063–7094

Sadek MM, Khalifa AS, Mostafa MGM (2017) Robust video steganography algorithm using adaptive skin-tone detection. Multimed Tools Appl 76(2):3065–3085

Santhosh B, Meghana NM (2016) A robust, privacy preserving secret data concealing embedding technique into encrypted video stream. Information systems design and intelligent application, Springer, pp. 645–655

Shanableh T (2012) Matrix encoding for data hiding using multilayer video coding and transcoding solutions. Signal Process Image Commun 27(9):1025–1034

Shanableh T (2012) Data hiding in MPEG video files using multivariate regression and flexible macroblock ordering. IEEE Trans Inf Forensics Secur 7(2):455–464

Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mob Comput Commun Rev 5(1):3–55

Article   MathSciNet   Google Scholar  

Sheri S, Traoudas B (2017) Video brand storytelling, the rise of content marketing: a qualitative study exploring the antecedents of brand perceptions

Sherley AP, Amritha PP (2010) A compressed video steganography using TPVD. Int J Database Manag Syst 2(3):67–80

Shimanovsky B, Feng J, M. Potkonjak M (2002) Hiding data in DNA. Paper presented at International Workshop on Information Hiding, pp. 373–386

Shou-dao W, Chuang-bai X, Yu L (2009) A High Bitrate Information Hiding Algorithm for Video in Video. World Acad Sci Eng Technol 35:413–418

Sikora T (1997) MPEG digital video-coding standards. IEEE Signal Process Mag 14(5):82–100

Solutions N (2005) Invisible secrets 4. Invis, Secrets

Song G, Li Z, Zhao J, Tu H, Cheng J (2014) A video steganography algorithm for MVC without distortion drift. Paper presented at international conference on audio, language and image processing (ICALIP), pp. 738–742

Song G, Li Z, Zhao J, Hu J, Tu H (2015) A reversible video steganography algorithm for MVC based on motion vector. Multimed Tools Appl 74(11):3759–3782

Stanescu D, Stratulat M, Ciubotaru B, Chiciudean D, Cioarga R, Micea M (2007) Embedding data in video stream using steganography. Paper presented at 4th international symposium on applied computational intelligence and informatics. pp. 241–244

Steganography — the new intelligence threat EWS 2004 subject area intelligence marine corps war college , Marine Corps University , Marine Corps, 2004

Stütz T, Autrusseau F, Uhl A (2013) Inter-frame H. 264/CAVLC structure-preserving substitution watermarking. University of Salzburg

Su PC, Lu MT, Wu CY (2013) A practical design of high-volume steganography in digital video files. Multimed Tools Appl 66(2):247–266

Sudeepa KB, Raju K, R. K. HS, Aithal G, (2016) A new approach for video steganography based on randomization and parallelization. Procedia Comput Sci 78:483–490

Sullivan GJ, Ohm J-R, Han W-J, Wiegand T et al (2012) Overview of the high efficiency video coding (HEVC) standard. IEEE Trans circuits Syst video Technol 22(12):1649–1668

Sur A, Krishna SVM, Sahu N, Rana S (2015) Detection of motion vector based video steganography. Multimed Tools Appl 74(23):10479–10494

Suresh M, Sam IS (2020) Optimized interesting region identification for video steganography using Fractional Grey Wolf Optimization along with multi-objective cost function. J King Saud Univ Inf Sci

Suttichaiya A, Sombatkiripaiboon Y, Imtongkhua P, Poonriboon C, So-In C, Horkaew P (2017) Video steganography with LSB color detection. J Telecommun Electron Comput Eng 9(2–2):23–28

Swain G (2019) Two new steganography techniques based on quotient value differencing with addition-subtraction logic and PVD with modulus function. Optik (Stuttg) 180:807–823

Swanson MD, Zhu B, Tewfik AH (1997) Data hiding for video-in-video. Proceedings of the International Conference on Image Processing. Vol. 2, pp. 676–679

Tasdemir K, Kurugollu F, Sezer S (2013) Video steganalysis of LSB based motion vector steganography. Paper presented at 4th European workshop on visual information processing (EUVIP). pp. 260–264

Tasdemir K, Kurugollu F, Sezer S (2016) Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes. IEEE Trans Image Process 25(7):3316–3328

Article   MathSciNet   MATH   Google Scholar  

Thomas TL (2003) Al Qaeda and the internet: the danger of" cyberplanning". Parameters 33(1):112–123

Torkaman MRN, Nikfard P, Kazazi NS, Abbasy MR, Tabatabaiee SF (2011) Improving hybrid cryptosystems with DNA steganography, Paper presented at international conference on digital enterprise and information systems. pp. 42–52

Tudor PN (1995) MPEG-2 video compression. Electron Commun Eng J 7(6):257–264

Video HAVCC, Tew Y, Wong K (2014) An overview of information hiding in H. 264/AVC compressed video. Circuits Syst Video Technol IEEE Trans 24(2):305–319

Wahab OFA, Badawy MB, Elshakankiry OA, El-sayed HS (2015) Utilizations of reversible lossless data hiding techniques in video sequences. Int J Comput Netw Technol 3(1)

Wang Z, Lu L, Bovik AC (2004) Video quality assessment based on structural distortion measurement. Signal Process Image Commun 19(2):121–132

Wang K, Zhao H, Wang H (2014) Video steganalysis against motion vector-based steganography by adding or subtracting one motion vector value. IEEE Trans Inf Forensics Secur 9(5):741–751

Wang K, Han J, Wang H (2014) Digital video steganalysis by subtractive prediction error adjacency matrix. Multimed Tools Appl 72(1):313–330

Wang P, Cao Y, Zhao X (2017) Segmentation based video steganalysis to detect motion vector modification. Secur Commun Netw 2017:1–12

Wang Y, Cao Y, Zhao X, Xu Z, Zhu M (2018) Maintaining rate-distortion optimization for IPM-based video steganography by constructing isolated channels in HEVC. In: Proceedings of the 6th ACM workshop on information hiding and multimedia security. pp. 97–107

Wang J, Jia X, Kang X, Shi Y-Q (2019) A cover selection HEVC video steganography based on intra prediction mode. IEEE Access 7:119393–119402

Warfare C, Wang H, Wang S (2004) Cyber warfare: steganography vs. steganalysis. Commun ACM 47(10):76–82

Westfeld A, Pfitzmann A (2000) Attacks on steganographic Systems. Inf Hiding 1768:1–16

Wiegand T, Sullivan GJ, Bjontegaard G, Luthra A (2003) Overview of the H. 264/AVC video coding standard. IEEE Trans circuits Syst video Technol 13(7):560–576

Wong K, Tanaka K, Takagi K, Nakajima Y (2009) Complete video quality-preserving data hiding. IEEE Trans Circuits Syst Video Technol 19(10):1499–1512

Xu C, Ping X (2007) A steganographic algorithm in uncompressed video sequence based on difference between adjacent frames. In Proceedings of the 4th International Conference on Image Graph ICIG 2007. pp. 297–302

Xu C, Ping X, Zhang T (2006) Steganography in compressed video stream. Paper presented at First International Conference on innovative computing, information and control, ICICIC. vol. 1, pp. 269–272

Xu X, Dong J, Tan T (2012) Universal spatial feature set for video steganalysis. In: Proceedings of the 19th IEEE international conference on image processing. pp. 245–248

Xu D, Wang R, Shi YQ (2014) Data hiding in encrypted H.264/AVC video streams by codeword substitution. IEEE Trans Inf Forensics Secur 9(4):596–606

Xu D, Wang R, Shi YQ (2016) An improved scheme for data hiding in encrypted H.264/AVC videos. J Vis Commun Image Represent 36:229–242

Xue Y, Zhou J, Zeng H, Zhong P, Wen J (2019) An adaptive steganographic scheme for H. 264/AVC video with distortion optimization. Signal Process Image Commun 76:22–30

Yadav P, Mishra N, Sharma S (2013) A secure video steganography with encryption based on LSB technique. Paper presented at IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) pp. 1–5

Yang M, Bourbakis N (2005) A high bitrate information hiding algorithm for digital video content under H.264/AVC compression. Midwest Symp Circuits Syst 2005:935–938

Yang Y, Li Z, Xie W, Zhang Z (2019) High capacity and multilevel information hiding algorithm based on pu partition modes for HEVC videos. Multimed Tools Appl 78(7):8423–8446

Yao Y, Zhang W, Yu N, Zhao X (2015) Defining embedding distortion for motion vector-based video steganography. Multimed Tools Appl 74(24):11163–11186

Yao Y, Zhang W, Yu N (2016) Inter-frame distortion drift analysis for reversible data hiding in encrypted H.264/AVC video bitstreams. Signal Processing 128:531–545

Ye H, Zhang W, Yao Y, Kong C, Huang H, Yu N, “Motion vector-based video steganalysis using spatial-temporal correlation. In: Proceedings of the 6th international congress on image and signal processing (CISP). Vol. 1. pp. 148–153

Yeh H-L, Gue S-T, Tsai P, Shih W-K (2014) Reversible video data hiding using neighbouring similarity. IET Signal Process 8(6):579–587

Zhai L, Wang L, Ren Y (2017) Combined and calibrated features for steganalysis of motion vector-based steganography in H. 264/AVC. In: proceedings of the 5th ACM workshop on information hiding and multimedia security. pp. 135–146

Zhang W, Cheung S.-CS, Chen M (2005) Hiding privacy information in video surveillance system. Paper presented at IEEE international conference on image processing. Vol. 3. p II–868

Zhang H, Cao Y, Zhao X (2015) Motion vector-based video steganography with preserved local optimality. Multimed Tools Appl 89:1–17

Zhang Y, Zhang M, Niu K, Liu J (2015) Video steganography algorithm based on trailing coefficients. Paper presented at international conference on intelligent networking and collaborative systems (INCOS), pp. 360–364

Zhao Y, Zhang H, Cao Y, Wang P, Zhao X (2015) Video steganalysis based on intra prediction mode calibration. International Workshop on Digital Watermarking. pp. 119–133

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Acknowledgements

This research work is supported by Technical Education Quality Improvement Project III (TEQIP III) of MHRD, Government of India assisted by World Bank under Grant Number P154523 and sanctioned to UIET, Panjab University, Chandigarh (India).

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Mukesh Dalal & Mamta Juneja

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Dalal, M., Juneja, M. A survey on information hiding using video steganography. Artif Intell Rev 54 , 5831–5895 (2021). https://doi.org/10.1007/s10462-021-09968-0

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Accepted : 29 January 2021

Published : 09 February 2021

Issue Date : December 2021

DOI : https://doi.org/10.1007/s10462-021-09968-0

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