To accommodate covert communications based on generated images, researchers hid secret information into the image generation process in an endogenous manner. Existing methods often degrade model performance by altering the image generation process or model parameters. To address these limitations, we propose a secure diffusion steganography framework in text-guided generative images (SDS-TG) based on Stable Diffusion, embedding secret information into an imperceptible latent space, thereby generating high-quality images without compromising model performance. We establish a reversible mapping mechanism between secret information and standard Gaussian noise, seamlessly integrating it into the conditional image generation process. Additionally, we fine-tune the encoder part of the diffusion models, enhancing the robustness of the steganographic algorithm and improving the accuracy of information extraction, without altering the image generation process. Experiments demonstrate that this approach offers high security and surpasses SOTA methods in terms of robustness and capacity.

With the widespread adoption of smart devices and social networking platforms, the development of robust image steganography techniques for public lossy channels has become increasingly crucial. Among JPEG-resistant steganographic methods, PMAS (Postprocessing and precise dither Modulation based robust Adaptive Steganography) has demonstrated superior performance by utilizing high-quality images and maintaining resilience against aggressive compression. This method achieves remarkable concealment in user-shared images, presenting substantial challenges to public communication security. To counter this threat, we propose a specialized lightweight Scale-Free Network for mining Clues in downward JPEG-resistant steganography (SF-ClueNet), specifically designed to identify vulnerabilities in PMAS despite its sophisticated anti-detection mechanisms. Departing from conventional approaches that depend on high-pass filter residuals, SF-ClueNet extracts comprehensive global statistical features, enabling effective detection of dispersed steganographic artifacts. When integrated with lightweight residual feature miner, our method maintains pattern recognition capabilities as image dimensions increase, ensuring consistent detection performance. Experimental results demonstrate that SF-ClueNet significantly enhances detection accuracy, exhibits robust performance against data distribution shifts with minimal transfer loss, and supports direct analysis of high-resolution images. These advanced capabilities position SF-ClueNet as a viable and efficient solution for practical steganalysis applications across diverse operational environments.

Existing video watermarking embeds robust watermarks in each frame of the video for copyright protection and tracking. However, just as any content written on a blank paper is easily perceived, embedding watermarks in the texture-poor frames impairs imperceptibility. Common geometric attacks such as scaling and rotation pose a significant challenge to the existing video watermarking. Image watermarking based on moments is robust against geometric attacks. However, moment-based watermarking is difficult to migrate to the video due to its lack of perceptual guarantee and high computational cost. In this paper, we propose an adaptive video watermarking scheme by exploring the relationship between moments and video textures, which can adaptively select texture-rich frames to embed watermarks for perceptual guarantee. Furthermore, we utilize the properties of moment calculation in videos to optimize efficiency. Extensive experiments show that the proposed method can achieve better imperceptibility than existing methods while maintaining strong robustness.

Malicious video tampering and unauthorized distribution on social networks pose significant challenges in distinguishing authentic from altered content and raise copyright infringement concerns. Although numerous watermarking schemes have been developed to protect copyright, tamper detection and localization have not been adequately addressed. To this end, we propose RDFMark, a dual-functional solution offering a unified framework for copyright protection and tamper detection. RDFMark introduces a new paradigm in video tamper detection by embedding a robust watermark that survives diverse attacks, enabling the extraction of copyright information and tampering cues. Extensive experiments validate the effectiveness of RDF-Mark against common video tampering scenarios encountered during social network transmissions, such as masking, barrage insertion, and cropping. Furthermore, it demonstrates resilience to geometric distortions, strong compression, re-compression, and composite attacks on social networks.

It is increasingly common for people to share videos on social platforms such as TikTok, YouTube and bilibili. The shared videos, however, is usually affected by lossy channel compression, thus a robust video watermarking based on discrete sine transform (DST) is proposed. Firstly, We divide each video frame into blocks and perform DST in embedding region, then the high-frequency component of DST is selected for watermark embedding. For further strengthening the robustness of lossy channel compression, a signed odd-even interval quantization method is designed by improving the odd-even quantization method. As a consequence of uncomplicated feature of DST, the computational efficiency of this algorithm is efficient, making it ideal for application in real-time scenarios such as video live broadcast and video conference. Moreover, the watermark is extracted without the host video. Experimental results show that the proposed algorithm not only ensures excellent video quality, but also significantly improves the robustness of lossy channel compression, and the robustness to different video contents is stable.