From Covert Hiding To Visual Editing: Robust Generative Video Steganography

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-06 18:09:05

Abstract: 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 excel in balancing security and capacity, they lack adequate robustness against common distortions in online social networks (OSNs). In this paper, we propose an end-to-end robust generative video steganography network (RoGVSN), which achieves visual editing by modifying semantic feature of videos to embed secret message. We exemplify the face-swapping scenario as an illustration to demonstrate the visual editing effects. Specifically, we devise an adaptive scheme to seamlessly embed secret messages into the semantic features of videos through fusion blocks. Extensive experiments demonstrate the superiority of our method in terms of robustness, extraction accuracy, visual quality, and capacity.

Main paper: 
 introduction: 
 Steganography is the science and technology of embedding secret messages into natural digital carriers, such as image [6, 41, 45] , audio [11, 27] , video [13, 24, 30] , text [1, 9, 30] , etc., which is a crux of covert communication system. Different from the message concealment of cryptography, steganography focuses on concealing the existence of secret messages. Generally, the natural digital carriers are called "cover" and the digital media with secret message are called "stego". Conventional image steganography methods [41, 43, 45] 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 Steganography.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 [5] , coding mapping [24] , or adaptive distortion function [17] , exploit video data redundancy for information hiding. While exhibiting commendable security and embedding capabilities, 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. These shortcomings emphasize the urgent need for further research to enhance the resilience of these methods, ensuring the reliable concealment and transmission of sensitive information in practical settings.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 editing. The highlevel 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 (RoGVSN), which consists of four modules, containing information encoding module, secret message embedding model, attacking layer, and secret message extraction module. For evaluation purpose, we employ face-swapping technology as a representative example to demonstrate the effectiveness of the proposed method. Fig. 1 illustrates the application scenario of RoGVSN. By leveraging secret messages to modulate semantic feature, such as facial identity information in videos, RoGVSN can achieve steganography during the process of visual editing. The applicability of our RoGVSN can be readily extended to various other applications. Comprehensive experiments demonstrates that our method outperforms existing state-of-the-art techniques. Notably, our approach exhibits commendable robustness against various forms of distortions and possesses strong generalization capabilities across diverse scenarios and datasets. These experiments underscore the efficacy and versatility of our method in addressing challenges in the field of visual editing and steganography.The main contributions of our proposed method are as follows,• 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. • The proposed method is robust against common distortions in social network platforms and the secret message can be extracted accurately. • Our method achieves better security than other state-ofthe-art methods, which can effectively evade the detection of steganalysis system. 
related work 2.1 image steganography: 
 Traditional image steganography techniques predominantly involve making alterations to embed secret message on the pixel domain. The Least Significant Bits substitution method [6] operates under the assumption that human eyes cannot perceive changes in the least significant bit of pixel values. HUGO [18] is a highly secure steganography system designed to minimize distortion to high-dimensional multivariate statistics. Syndrome-Trellis Coding (STC) [14] utilizes predefined embedding costs for all pixels or discrete cosine transform (DCT) coefficients. A pioneering advancement, HiDDeN [45] introduces an end-to end trainable framework through an encoder-decoder architecture based on deep networks. SteganoGAN [43] employs dense encoders to enhance payload capacity. Volkhonskiy et al. [33] demonstrates promising performance in both the authenticity of generated images and resistance to steganalysis systems. With the development of generative adversarial networks, a wave of work on generative steganography has emerged. For instance, Wei et al [37] propose an advanced generative steganography network that can generate realistic stego images without using cover images. IDEAS [26] disentangles images into structural and texture vectors, subsequently embedding secret message into the structural vector. However, these image steganography methodologies can be obliterated by common postprocessing operations, such as JPEG compression or Gaussian Blur.Early video steganography approaches frequently involved direct modification of the RGB or YUV color space to embed secret message. For instance, Cetin et al. [5] compute frame-specific histogram features and established a threshold for secret information embedding within RGB pixels. Subsequently, Dong Y et al. [13] discover that within the HEVC codingmodel, altering the intra-frame mode primarily affects video coding efficiency, while modifying the multilevel recursive coding unit does not significantly amplify this distortion's impact. Liu et al. [24] utilize diamond-shaped coding to enhance the expressive capability of the PU division pattern for concealed information, effectively boosting the payload. They also replace the loop filter of the I frame with a CNN to enhance the reconstruction quality of compressed images. PWRN [22] refine this approach by employing a super-resolution convolutional neural network with a wide residual-net filter to replace the loop filter in HEVC. More recently, He et al. [17] devise an adaptive distortion function based on improved Rate Distortion Optimization and adopted Syndrome-Trellis Code [35] steganography coding to minimize embedding distortion. They also propose a super-resolution CNN with Non-Local Sparse Attention-net Filter to replace the loop filter in HEVC, thereby reconstructing the reference frame and enhancing visual quality. However, these methodologies exhibit limited robustness, rendering the concealed secret message susceptible to inadvertent loss during subsequent compression coding. Moreover, they grapple with the challenge of mitigating the impact of diverse distortions that may arise during lossy channel transmission. Inspired by semantic-based image steganography [26, 44] schemes, we propose RoGVSN, a novel approach that robustly transmits secret message through the modification of identity feature within facial videos.Visual editing encompasses a diverse array of manipulations, ranging from basic adjustments to complex transformations. Such manipulations can include color correction to enhance or modify the appearance of individual elements within the images [29] , as well as the deletion, addition [40] , or alteration of objects to either enhance the composition or convey a different narrative [38] . Additionally, techniques like image blending or compositing enable the seamless integration of multiple photographs to generate composite images with enhanced aesthetic or communicative value. In the realm of videos, visual editing extends to a broader range of manipulations, including the application of effects to specific frames to enhance visual impact or convey specific emotions. Furthermore, it involves the removal or addition of elements within the video to alter the narrative or aesthetic presentation. Notably, face-swapping techniques have gained prominence, allowing for the replacement of one person's face with another's, thereby enabling novel creative possibilities or facilitating various applications such as identity protection or entertainment [7] . 
methodology 3.1 overview: 
 In this section, we will describe the framework of our proposed RoGVSN. Our method aims to embed secret message 𝑴 using semantic feature extracted from the reference image 𝑰 𝑅 into the cover video 𝑽 𝐶 , generating the stego video 𝑽 ′ 𝐶 . Subsequently, various distortions such as video compression, noise addition, etc., can be applied to the stego video 𝑽 𝐶 . The stego video 𝑽 𝐶 is then transmitted to the receiver. A trained secret message extractor is capable of mapping each frame of the stego video 𝑽 𝐶 back to the secret message 𝑴 ′ . As illustrated in Fig. 2 , our approach comprises four modules: Information Encoding Module, Secret Message Embedding Module, Attacking Layer, Secret Message Extraction Module.The information encoding module consists of three parts: The first is identity extractor (𝑬 𝑖𝑑 ) which is responsible for extracting the specific feature representation tailored for the reference image (𝑰 𝑅 ). In the proposed method, we leverage the facial recognition network [10] as the identity extractor. The second is video feature extractor 𝑬 𝜙 . It acquires the latent representation of cover video 𝑽 𝐶 with 𝑣 frames, employing an encoder [7] for video feature extraction. The third is secret message encoder (𝑬 𝑚 ) which is a multilayer perceptron (MLP) with one dense layer. The above three parts are formulated as follows,EQUATIONEQUATIONEQUATIONwhere 𝑰 𝑖 𝐶 denotes the 𝑖-th frame image of the cover video. 𝑭 𝑖 𝐶 ∈ R 𝐶 ×𝐻 ×𝑊 represents the latent feature representation of 𝑖-th frame image. 𝑭 𝑖𝑑 ∈ R 1×512 denotes the identity feature matrix of the reference image. 𝑴 ∈ R 1×𝑚 is the secret messages. 𝑾 𝒎 ∈ R 𝑚×512 and 𝒃 𝑚 ∈ R 1×512 denotes the learnable weights and biases.The purpose of the secret message embedding module is to embed the secret message during the face swapping scenario. The key problem is how to implement face swapping under the guidance of secret messages. To the best of our knowledge, the latent features of cover video contain the identity feature and attribute feature. The essence of face swapping is that the identity of cover video is replaced with that of reference image. As a result, we embed the secret messages into the identity feature of reference image, which is formulated as follows,EQUATIONwhere 𝜆 is a hyper-parameter adjusting the influence of secret message on identity feature.Since the identity and attribute features are highly coupled so we cannot directly extract attribute feature from the latent feature representation 𝑭 𝑖 𝑐 extracted by 𝑬 𝜙 . To better preserve the attributes, we design a Secret-ID block that consists of the modified version of the residual block [16] and the adaptive instance normalization [20] to inject 𝑭 ′ 𝑖𝑑 into 𝑭 𝑖 𝑐 . The Secret-ID block is formulated as follows,EQUATIONwhere 𝜇 (𝑭 secret messages is extracted. The secret message extraction module can be formulated as follows,EQUATIONwhere 𝑴 ′ is the extracted secret messages. 𝑬 𝑒𝑥𝑡 (•) denotes the secret message extraction module. 𝑽 𝑆 is the stego video.To bolster the robustness of our method for the stego videos in real-world scenarios, we design an attacking layer. This module simulates prevalent distortions encountered across social network platforms, including but not limited to compression artifacts, noise interference. By subjecting the stego videos to these common distortions, our approach are more resilient to the challenges posed by real-world transmission environments. This proactive approach ensures the embedded secret message remains intact and retrievable even in the presence of various forms of degradation. JPEG compression. JPEG compression is a common image compression method used in the process of photo storage. However, the quantization step is a non-differentiable because of the rounding process. To address this issue, we adopt the approach introduced by Shin et al. [32] for approximating the quantization step near zero with the function Eq. ( 7):EQUATIONwhere 𝑥 denotes a specific pixel 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 distortions that are generally brought by transmission on the Internet and the artificial processing on videos.For the two distortions, we can perform a linear transformation on the pixels of each channel as the formula Eq. ( 8).EQUATIONwhere 𝒑(𝒙) refers to the distorted and 𝒇 (𝒙) refers to the original image. 𝒂 and 𝒄 are hyper-parameters to control contrast and brightness, respectively. Blur. Blur is a common distortion yielded by the video processing software and the transmission on social network platforms. To simulate blur, we randomly select an angle and generate a linear blur kernel with a width ranging from 3 to 7 pixels. Saturation. We perform random linear interpolation between RGB image and its grayscale equivalent to simulate the distortion.Noise. We use Gaussian noise to simulate any other distortions that are not considered in the distortion module. We employ a Gaussian noise model (sampling the standard deviation 𝛿 ∼ 𝑈 [0, 0.2]) to simulate imaging noise.The proposed method not only ensures the quality of generated stego videos, but also guarantees the accurate extraction of secret messages. Therefore, we leverage four loss functions and a penalty to train the proposed modules, containing identity loss, attribute loss, adversarial loss, secret loss, and gradient penalty. Identity Loss. The Identity Loss aims to constrain the difference between the identity feature (𝑭 𝑖𝑑 ) of reference image and the identity feature ( F 𝑖 𝑖𝑑 ) of the 𝑖-th frame image in generated stego video. This constraint minimizes the identity modification introduced by the secret messages, consequently enhancing the quality of the generated stego video. In this part, we leverage cosine similarity to calculate the difference, which is expressed by the formula Eq. (9) .EQUATIONAttribute Loss. We use the weak feature matching loss [7] to constrain attribute difference before and after embedding secret messages. This loss utilizes the Discriminator [34] to extract multiple layers of features from the ground truth image and the generated result. The loss function is defined as Eq. (10) .EQUATIONwhere 𝑫 𝑗 represents the feature extractor of Discriminator D for the j-th layer, 𝑁 𝑗 signifies the number of elements within the jth layer, and 𝐻 denotes the total number of layers. 𝑰 𝑖 𝑆 stands for the 𝑖-th frame of the generated stgeo video 𝑽 𝑆 , while 𝑰 𝑖 𝐶 refers to the corresponding ground truth image. Additionally, ℎ denotes the layer from which we initiate the computation of the weak feature matching loss. Adversarial Loss and Gradient Penalty. In the proposed method, to enhance the quality of the generated stego videos, we utilize a discriminator [34] during the training process. We adopt the Hinge version [3, 25, 28] of the adversarial loss defined as Eq. ( 11):EQUATIONwhere 𝑫 denotes the Discriminator, 𝑥 and 𝑧 in our method is respectively 𝑰 𝑅 and 𝑰 𝑖 𝑆 Furthermore, we incorporate the Gradient Penalty term [2, 15] to effectively counteract the risk of gradient explosion within the Discriminator. The Gradient Penalty is in Eq. (12) .EQUATION)where 𝑧 is the 𝑖-th frame 𝑰 𝑖 𝑆 of the stego video. Secret Loss. The process of secret message extraction can be framed as a binary classification problem. To address this, we employ the Binary Cross-Entropy loss (BCE), which is articulated in Eq. (13) . In this equation, 𝑥 represents the ground truth secret, while x denotes the extracted secret.EQUATIONLastly, we employ the secret loss to effectively regulate the process of secret message extraction. This loss function is defined by Eq. ( 14).EQUATIONTotal loss. The total loss of our method is defined as follows,EQUATIONwhere 𝛼 1 = 10, 𝛼 2 = 10, 𝛼 3 = 15, and 𝛼 4 = 10 -5 . 
experiments and discussions 4.1 experimental setups: 
 Datasets. We use Vggface2 [4] for training and FFHQ [21] for validation. We crop and resize facial areas to a fixed 224 × 224 resolution for input images. To analyze quality and performance, we randomly select 100 videos from DeepFake MNIST+ [19] to evaluate the performance. Implementation Details. We train the model to encode a binary message of length 𝑚 = 9 or 18 bits in a frame. During training, we employ Adam optimizer with a learning rate of 4 × 10 -4 and a batch size of 4. The parameters 𝐶, 𝑊 , 𝐻 in 𝑭 𝑖 𝐶 are 𝐶 = 512, 𝑊 = 28, 𝐻 = 28. The networks train for 1 million steps. After 800k steps, we introduce all attack types proposed by the Attacking Layer. The purpose of this approach is to ensure both the visual quality and robustness of the generated stego videos. We use an NVIDIA GeForce RTX 3090 GPU for our experiments. To mitigate such phenomena, we incorporate the concept of adversarial training [8, 21, 25] , employing the Discriminator to discern outcomes with noticeable errors. We adopt the patchGAN [39] version of the Discriminator. 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 [23, 31, 42] to demonstrate our method's antidetection capability. Baselines. To ensure fair comparison, we align HiDDeN and LSB to this capacity. For the HiDDeN method, we use its own noise layer, in which we employ two types of noise: JPEG compression and resize (0.7-0.8). During the training phase, we embed 18 bits secret messages into per image to train the final model. This method allows each pixel to conceal up to 3 bits of data. For the LSB method, we exclusively modifies the least significant bits of the initial six pixels across the three channels. Consequently, our capacity equates to 18 bits for each frame within each test video. In addition, we embed secret message according to PWRN's paper [22] . Due to its PU based design, the capacity of PWRN is limited to 15BPF when adjusting the size of the input image to 224 × 224.We compare the performance of our RoGVSN with image-level steganography including HiDDeN [45] and LSB [6] and video-level steganography including PWRN [22] . Video Quality Assessment. Fig. 3 shows qualitative results on the integrity of generated stego video frames. We perform tests within and across datasets, each containing 16 test samples. The left set of 16 samples are test images from the within VGGFace2 dataset, while the right set of 16 samples are cross-dataset test images from the FFHQ dataset. The generated faces effectively change individual identities while retaining attributes like expressions and poses. Fig. 4 illustrates the visual effects of certain intermittent frames within the stego videos. Fig. 4 shows that our method achieves visual editing by embedding secret message while injecting the identity information of the reference images into the cover videos. Furthermore, the results of the stego video frames on the right three columns exhibit excellent visual quality.We also employ ID Similarity and VMAF two metrics to quantitatively assess the quality of stego videos produced by our model. Qualitative and quantitative analysis results in Table 3 . The ID Similarity gauges the resemblance of identity feature between the generated stego video and the reference facial image. This entails calculating the cosine value between the two identity features. A on severity level 1. Table 2 presents the experimental results of extraction accuracy whe the severity level is 2. The results show that our method can successfully extract secret message with high accuracy even after severe distortions. LSB [6] struggles even with PNG (quantization) and HiDDeN [45] , though trained with a distortion module, can not generalize well to video-level distortions. For each example, we extract three frames from the corrupted video using uniform sampling, with a sampling interval of three frames. Detection method No-embedding RoGVSN CADDM [12] 0.8514 0.8403 AltFreezing [36] 0.9958 0.9923Security Analysis. We use three video steganalysis tools to evaluate the security of our method. Li et al. [23] utilize the recompressed quantity change ratio of 25 kinds of Prediction Units (PUs) in the P slices as the 25-dimensional feature. Sheng et al. [31] extract 6dimensional features from the rate of change in the number and ratio of 4 × 4, 8 × 8, and 16 × 16 PUs following I-slice recompression. Zhai et al. [42] propose to leverage low motion vector (MV) consistency within overlay videos, which exhibits high detection accuracy across multiple steganographic videos based on PU mode and MV. After constructing the above features for video steganalysis, we choose the support vector machines (SVMs) as the classifier. For each steganalysis test, the detection accuracies are averaged over 20 iterations. The detection performance of these three steganalysis schemes is presented in Table 5 . It demonstrates that our method exhibits the best security compared to the three counterparts.We also employ two state-of-the-art deepfake detection methods to assess the impact. The experimental evaluation is conducted on a dataset consisting of 300 videos, comprising 100 real videos and 100 fake videos with and without steganography embedding. Specifically, "No-embedding" and "RoGVSN" separately denote the cover videos and the stego videos. The results from the evaluation are summarized in Table 7 . The findings suggest that our proposed method has minimal impact on the performance of deepfake detection. This implies that even with the embedding of secret messages using our method, the ability of deepfake detection algorithms to distinguish between real and fake videos remains largely unaffected.Embedding Position of Secret Message. Within our generation network, consisting of 9 Secret-ID blocks, we aim to examine varying positions for embedding the secret message. We partition the secret message into two 9-bit segments and determine their respective positions, as follows,• 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.• Setting (RoGVSN): 1st-3rd blocks and 4th-6th blocks.The detection performance across these four configurations is presented in Table 4 . Setting (b) and (c) exhibit a substantial reduction when compared to those of Setting (a) and Setting (RoGVSN), implying that augmenting the quantity of injected secret information blocks enhances performance. Moreover, Setting (c) surpasses the performance of Setting (b), potentially due to the greater impact of subsequent blocks on the final generated image. Ablation on Attacking Layer. Fig. 7 compares the results under two training scenarios: with and without distortion module. We can observe that even without distortion module, our method still possesses a certain degree of robustness, achieving extraction accuracies surpassing 0.86, which already exceeds the performance of the three comparative methods. Upon the application of distortion, the accuracy is on average improved by 6%. Ablation Results on 𝜆 and the Discriminator. The balance between visual quality and extraction accuracy is controlled by the hyperparameter 𝜆. Selecting an appropriate value for 𝜆 is crucial. We conduct ablation experiments on 𝜆. Fig. 6 shows the visual quality of stego videos and Table 8 shows the extraction accuracy of secret message when 𝜆 is different. These results indicate that setting 𝜆 to 0.01 can achieve a satisfactory balance between visual quality and extraction accuracy. We also conduct ablation experiments on discriminator. Fig. 6 indicates the introduction of discriminator significantly enhances visual quality of stego videos. 1 . 
conclusion and discussion: 
 In this paper, we propose a method for covert transmission in online social network. Experimental results demonstrates our approach can generate high-quality stego videos and ensure accurate extraction of secret message even when subjected to various forms of distortions. The flexibility of model training makes it difficult for attackers to detect our secret message. Traditional video steganography methods leverage redundancy in video data for hiding. Although these methods have certain security and embedding capabilities, they notably lack robustness. This deficiency makes the hidden secret message susceptible to loss during compression encoding and channel transmission. Our method based on visual editing of videos can be seen as a process of modifying the semantic feature of objects within the videos. Instead of hiding secret messages in covert space, we embed them into the semantic feature of the video during visual editing. Advanced semantic feature is less susceptible to distortion, rendering this method inherently robust. Additionally, we introduce an attacking layer, which further enhances the robustness of our method. The RoGVSN is jointly trained with four modules, containing information encoding module, secret message embedding model, attacking layer, and secret message extraction module. Experimental results of RoGVSN method applied to facial video datasets demonstrate its superiority over existing video and image steganography techniques in terms of both robustness and generalization capacity. Our work is a preliminary exploration of the robust generative video steganography. In the future, we will comprehensively investigate the robustness of generative video steganography.  
 
 Appendix:  


Decision: The submission received mixed reviews (accept, weak reject, accept, borderline accept). The main contribution in this work is the presentation of a generative video steganography method. A majority of reviewers regarded this work as a novel addition to existing steganography literature, with clear technique contribution and empirical evaluation. However, a few reviewers also pointed out several key weaknesses such as limited applications. Based on all reviews and correspondence, the ACs would recommend to accept this work, meanwhile suggesting the authors to revise it accordingly.

Reviews: review: paper_summary: paper_summary: The paper proposes a  generative video steganography method (RoGVSN) which generates a stego video, by embedding a secret message using semantic features extracted from a reference image.  As experimentation, The robustness of RoGVSN is evaluated using ID Similarity and VMAF.

Extraction accuracy is quantified using BPF (Bits Per Frame), compared with image level steganography methods, HiDDeN and LSB, and video level steganography method PWRM. This evaluation of extraction accuracy encompasses employing distortions, under two severity levels, and the application of video compression techniques. 

For security analysis, the paper uses three video steganalysis tools to compare the AUC of RoGVSN with the same steganography methods used in the extraction accuracy evaluation. Furthermore, the AUC of cover video and stego videos of RoGVSN, under the deepfake detections mehods, CADDM and AltFreezing, are evaluated. 

Finally, An ablation study is conducted to examine the  performance of the method across varying positions for embedding the secret message, across 4 different configurations.
strength_weakness: strength_weakness: An innovative study of steganography from the niche of utilising deepfake.

Fig.2 of the framework clearly explains the different aspects of the methodology.
limitations: limitations: The payload of hidden secret messages is limited (9 or 18bits). There is very little discussion.

The explanation of the actual meaning needs to be more convincing, beyond the social platforms. Meaningful application scenarios can be described in more depth.

The dataset on the selection of secret message and the reference images lacks details.
paper_summary: paper_summary: The paper proposes an innovative approach to video steganography that embeds secret messages within the semantic features of videos, rather than modifying the covert space for embedding as in traditional methods.
It introduces an end-to-end robust generative video steganography network (RoGVSN) that achieves visual editing by modifying the semantic features of videos to embed secret messages.
The paper uses face-swapping as an illustrative example to demonstrate the visual editing effects.
strength_weakness: strength_weakness: 1. It shows innovation embedding secrets within the semantic features of videos, rather than the conventional approach of modifying the covert space.
2. It shows robustness against common distortions encountered on online social networks.
limitations: limitations: 1. Although this method uses face-swapping technology for data hiding against video compression, how can this steganography make a positive societal impact? From my understanding, this method modifies the semantic features and thus can not be used for the copyright protection of the original video. Face-swapping is also a technology that can be easily used for malicious editing. Does this method show any benefit against the abusive use of face-swapping technologies? 
2. The novelty is limited. The face-swapping pipeline is basically the same as sim-swap, and the secret message is embedded by a simple summation in the feature space. This is basically an easy fusion of the secret message embedding and the identity embedding.
paper_summary: paper_summary: This paper proposed an end-to-end robust generative video steganography network, RoGVSN. The proposed method achieves visual editing by modifying semantic features of videos to embed secret message for enhanced robustness.
strength_weakness: strength_weakness: 1. The writing is clear, and the review of related studies is sufficient. 
2. Adequate experiments and analysis demonstrate that proposed method is capable of generating high-quality stego videos and outperforms existing methods in terms of robustness and capacity.
limitations: limitations: 1. Please illustrate the motivation for embedding the secret message in identity features rather than in other attribute features.
2. There are some typo errors, e.g., “ where 𝜆 is a hyper-parameter…” should be “where 𝜆 is a hyper-parameter…”. 
3. Add the following reference:
Meng L, Jiang X, Sun T, et al. A Robust Coverless Video Steganography Based on the Similarity of Inter-Frames[J]. IEEE Transactions on Multimedia, 2023.
paper_summary: paper_summary: This paper introduces a novel video steganography technique that entails replacing facial features through video editing, also known as "Deepfake for steganography". Since this approach primarily emphasizes semantic features over low-level video or image features, it demonstrates a robust capability to withstand typical distortions and malicious attacks.
strength_weakness: strength_weakness: * A novel method of using facial editing techniques to transmit covert messages.
* Strong resilience to various distortion and malicious attacks, especially in typical social media scenarios.
* Sufficient experiments to demonstrate the efficacy and security of the proposed RoGVSN.
limitations: limitations: a) The security of the proposed method is severely compromised by the imperceptibility of Deepfake-like methods, posing an inevitable threat to the application of this method. The results in Table 7 also have indicated that the proposed RoGVSN faces a significant challenge that could be easily perceived as "abnormal". The detection of AI-generated content videos will become more common and easier in the future. Maintaining imperceptibility for facial editing is still challenging. This is the inherent drawback and may require further improvements.

b) This method has limited application as it can only process videos of humans. It would be more valuable if this method could be applied to a broader range of video types, including natural landscapes, news reports, sports events, and short videos, which are more prevalent.

c) Some other questions:
1. Details of the extraction algorithm are somewhat unclear. Is the extraction process an end-to-end classifier that directly extracts the secret message?
2. Distortion of the "Bit Error" type appears to severely impact the readability of the entire frame. How does the RoGVSN manage to survive such a chaotic situation when a significant portion of the frame image is corrupted?
3. The impact of $\lambda$ in Table 8 does not appear to follow a straightforward one-peak pattern. Results for $\lambda=1.0,0.01$ demonstrate notably superior performance compared to the other scenarios. Is there any explanation for this?
4. Steganalysis for stega-videos is somehow missing. Though this approach may differ from the previous method, it is still intriguing to determine if the created stego-videos could deceive the traditional steganalyzer to some extent.