Deep learning-based video watermarking algorithms perform well in terms of robustness and perceptual quality. However, their resistance to HEVC compression remains a major limitation, especially under high compression ratios, where watermark extraction accuracy significantly degrades. To address this issue, this paper proposes a Spatio-temporally Enhanced Video Watermarking (SEVMark) based on invertible neural networks (INNs). SEVMark introduces a channel attention mechanism in the temporal domain to adaptively focus on keyframes, and employs spatial pyramid pooling module in the spatial domain to capture multi-scale features. These two modules work in tandem to enhance the spatio-temporal feature representation, achieving high robustness and imperceptibility. Furthermore, based on the HEVC encoding process, a HEVC video compression simulator (DiffH265) is designed and incorporated as a key component of the noise layer, guiding the encoder-decoder network to maintain high extraction accuracy under HEVC compression. Experimental results demonstrate that SEVMark outperforms state-of-the-art methods in both quantitative and qualitative evaluations, particularly demonstrating excellent robustness against HEVC compression attacks under high compression ratios.

Video steganography in the intra prediction mode (IPM) domain embeds secret messages by modifying IPM values. However, such modifications are highly susceptible to detection by video steganalysis techniques, particularly those leveraging recompression-based calibration features. In this paper, the signal restoration phenomenon that occurs during video recompression is first modeled, which reveals the underlying reason for the effectiveness of recompression calibration-based detection features. Based on this insight, a IPM priority-preserving strategy is proposed. This strategy integrates the steganographic modification state with the optimal IPM selection mechanism during recompression, employing dynamic cost revision and joint cost decomposition to guide steganographic modifications toward optimal selection. By aligning modifications with recompression tendency, the proposed method mitigates signal restoration effects, reduces distribution discrepancies in calibration-based detection features, and enhances overall steganographic security. Extensive experimental evaluations demonstrate that the proposed scheme significantly improves resistance against both intra-frame and inter-frame steganalysis features while maintaining superior visual quality and bitrate control.

Video steganography based on prediction unit (PU) embeds secret messages by modifying PU partition modes, where such modifications typically relying on additive embed ding distortion. However, additive distortion cannot accurately reflect sample-level distortion variations caused by inter-frame distortion propagation, leading to distortion drift across frames and making the embedded traces more susceptible to detection by existing video steganalysis. To address this issue, we analyze the mechanism of inter-frame distortion propagation and propose a steganographic modification strategy based on the Synchronizing Mode Directions (SMDs), which effectively mitigates the accumulation and spread of inter-frame distortion. Furthermore, to enhance the security of stego videos, a video frame-level directional distortion propagation model is constructed based on Distortion Propagation Chains (DPC). By constructing the DPC model, the propagation of inter-frame distortion resulting from steganography can be traced directionally, enabling dynamic correction of the cover cost. Experimental results demonstrate that the proposed method significantly enhances resistance to both intra-frame and inter-frame steganalysis, while maintaining high visual quality and effective bitrate control.

With the wide spread of video, video watermarking has become increasingly crucial for copyright protection and content authentication. However, video watermarking still faces numerous challenges. For example, existing methods typically have shortcomings in terms of watermarking capacity and robustness, and there is a lack of specialized noise layer for High Efficiency Video Coding(HEVC) compression. To address these issues, this paper introduces a Deep Invertible Network for Video watermarking (DINVMark) and designs a noise layer to simulate HEVC compression. This approach not only increases watermarking capacity but also enhances robustness. DINVMark employs an Invertible Neural Network (INN), where the encoder and decoder share the same network structure for both watermark embedding and extraction. This shared architecture ensures close coupling between the encoder and decoder, thereby improving the accuracy of the watermark extraction process. Experimental results demonstrate that the proposed scheme significantly enhances watermark robustness, preserves video quality, and substantially increases watermark embedding capacity.

With high embedding capacity and security, transform coefficient-based video steganography has become an important branch of video steganography. However, existing steganalysis methods against transform coefficient-based steganography provide insufficient consideration to the prediction process of HEVC compression, which results in steganalysis that is not straightforward and fail to effectively detect adaptive steganography methods in low embedding rate scenarios. In this paper, an HEVC video steganalysis method based on centralized error and attention mechanism against transform coefficient-based steganography is proposed. Firstly, the centralized error phenomenon brought by distortion compensation-based steganography is analyzed, and prediction error maps is constructed for steganalysis to achieve higher SNR(signal-to-noise ratio). Secondly, a video steganalysis network called CESNet (Centralized Error Steganalysis Network) is proposed. The network takes the prediction error maps as input and four types of convolutional modules are designed to adapt to different stages of feature extraction. To address the intra-frame sparsity of adaptive steganography, CEA (Centralized Error Attention) modules based on spatial and channel attention mechanisms are proposed to adaptively enhance the steganographic region. Finally, after extracting the feature vectors of each frame, the detection of steganographic video is completed using the self-attention mechanism. Experimental results show that compared with the existing transform coefficient-based video steganalysis methods, the proposed method can effectively detect multiple transform coefficient-based steganography algorithms and achieve higher detection performance in low payload scenarios.

HEVC (High Efficiency Video Coding) provides abundant embedding carriers for video steganography, leading to rapid development in the field of video steganography while increasing the urgent demand for video steganalysis. However, existing steganalysis methods against PU (prediction unit) based steganography primarily use the extraction of video statistical features, which ignore the potential information of each frame and fail to effectively detect different PU-based steganography methods. In this paper, a video steganalysis method based on PU maps and multi-scale convolutional residual network is proposed. Firstly, the effects of PU-based steganography on the spatial domain and the compressed domain are analyzed. It is observed that steganography has less impact on the spatial domain, whereas it significantly disrupts the connection between PU blocks in the compressed domain, leaving distinct steganographic traces. Consequently, the PU partition modes containing local connections are introduced to generate PU maps for steganalysis. Secondly, a video steganalysis network called PUSN (Prediction Unit Steganalysis Network) is constructed. The network takes PU maps as input and consists of three parts: feature extraction, feature representation, and binary classification. Additionally, a multi-scale module is proposed to enhance the detection performance. Finally, the detection result of the steganographic video is obtained by the voting mechanism. The experimental results show that compared with the existing steganalysis methods, the proposed method could effectively detect multiple PU-based steganography methods and achieve higher detection accuracy across various embedding rates.

With the increasing popularity of digital video, video steganography has become a hot research topic in the field of covert communication and privacy protection. The existing prediction unit (PU) based video steganography often tends to result in large bit rate increase, which is also easily noticeable to the steganography analyst. To solve this problem, an adaptive steganography for HEVC video based on attention-net and PU partition modes is proposed. First, the distortion of modified PUs is analyzed from the perspective of rate distortion optimization at the group of pictures (GOP) level, and we find that modifying PU will lead to distortion accumulation and abnormal bitrate increase. Therefore, an adaptive distortion function based on the improved rate distortion cost is designed, and the embedding distortion is minimized by using Syndrome-Trellis Code (STC) steganography coding. Meanwhile, a super-resolution convolutional neural network with non-local sparse attention-net filter is proposed to replace the in-loop filter in HEVC to reconstruct the reference frame, thereby reducing the bitrate cost and improving the visual quality of stego-video. Experimental results show that the proposed algorithm can achieve superior perceptual quality and bitrate performance comparing with the sate-of-the-art works.

Distortion compensation method is a common way to cope with the distortion drift problem in coefficient domain HEVC steganography. However, it will leave obvious steganographic traces called centralized error (CER). The current coefficient domain HEVC steganography is fragile to CER-based steganalysis. In this article, a novel adaptive HEVC steganography that can resist CER-based steganalysis is proposed. First, the difference of CER between H.264/AVC and HEVC is introduced, and the CER feature in HEVC is re-modeled. Then, from two aspects of overall average distribution and single-frame distribution, we conclude that there is a strong correlation among four components of the CER feature. Last, an adaptive cost function is proposed by maintaining one component distribution to resist steganalysis. Experimental results show that the proposed cost function can effectively improve the security compared with other coefficient-based HEVC steganography. In addition, the proposed steganography outperforms other HEVC steganography in visual quality and bit rate increase.

Modification of the optimal recursive block encoding process is commonly adopted in HEVC steganography based on block partitioning structure to embed secret messages, which inevitably disrupts the optimal rate distortion optimization process, resulting in a degradation of visual quality and an increase in bit rate. In this paper, we analyze the intra frame recursive block encoding process, categorizing modifications based on block partitioning structures into skip-level and non-skip-level modifications. Then, the rate distortion difference between these two types is compared. Additionally, the Maintenance Principle of Quad-tree Structure is introduced, which aims to preserve the stego quad-tree structure as closely as possible to the original one. Furthermore, a new cover mapping method is designed to expand the embedding capacity, and a quad-tree structure-preserving adaptive steganography is proposed. Extensive experimental results demonstrate that the proposed scheme can embed messages with fewer disruptions to the optimal rate distortion optimization process, ultimately improving the visual quality and reducing the bit rate growth.