Robust steganography without embedding based on secure container synthesis and iterative message recovery

Synthesis-based steganography without embedding (SWE) methods transform secret messages to container images synthesised by generative networks, which eliminates distortions of container images and thus can fundamentally resist typical steganalysis tools. However, existing methods suffer from weak message recovery robustness, synthesis fidelity, and the risk of message leakage. To address these problems, we propose a novel robust steganography without embedding method in this paper. In particular, we design a secure weight modulation-based generator by introducing secure factors to hide secret messages in synthesised container images. In this manner, the synthesised results are modulated by secure factors and thus the secret messages are inaccessible when using fake factors, thus reducing the risk of message leakage. Furthermore, we design a difference predictor via the reconstruction of tampered container images together with an adversarial training strategy to iteratively update the estimation of hidden messages. This ensures robustness of recovering hidden messages, while degradation of synthesis fidelity is reduced since the generator is not included in the adversarial training. Extensive experimental results convincingly demonstrate that our proposed method is effective in avoiding message leakage and superior to other existing methods in terms of recovery robustness and synthesis fidelity.</p

Attack-defending contrastive learning for volumetric medical image zero-watermarking

Zero-watermarking is an emerging distortion-free copyright protection method for volumetric medical images. However, achieving both robustness against various malicious attacks and distinguishability between individual images remains challenging. In this article, we propose a novel attack-defending contrastive learning zero-watermarking (ADCL-ZW) scheme to tackle the above challenge using deep learning-based representations. In our approach, we design an attack-defending data enrichment mechanism to enhance the watermarking robustness by generating a large number of image samples under various watermarking attacks. Subsequently, features for both watermarking distinguishability and robustness are enhanced through application of a contrastive loss. In particular, we implement a dual-stream Siamese network architecture to effectively handle both signal attacks and geometric attacks in order to enhance the watermarking performance. Experimental results demonstrate that ADCL-ZW achieves stronger watermarking robustness and a better tradeoff between watermarking robustness and distinguishability compared with state-of-the art zero-watermarking methods. One of the highlighted metrics is that the false-negative rate of ADCL-ZW achieves 0.01 when a fixed false-positive rate is set to 1%, which is more than 13.3 times better than the benchmark methods.</p

Robust and discriminative zero-watermark scheme based on invariant features and similarity-based retrieval to protect large-scale DIBR 3D videos

Digital rights management (DRM) of depth-image-based rendering (DIBR) 3D video is an emerging area of research. Existing schemes for DIBR 3D video cause video distortions, are vulnerable to severe signal and geometric attacks, cannot protect 2D frames and depth maps independently, or have difficulty handling large-scale videos. To address these issues, a novel zero-watermark scheme based on invariant features and similarity-based retrieval to protect DIBR 3D video (RZW-SR) is proposed in this study. In RZW-SR, invariant features are extracted to generate master and ownership shares to provide distortion-free, robust and discriminative copyright identification under various attacks. Different from conventional zero-watermark schemes, our proposed scheme stores features and ownership shares correlatively and designs a similarity-based retrieval phase to provide effective solutions for large-scale videos. In addition, flexible mechanisms based on attention-based fusion are designed to protect 2D frames and depth maps, either independently or simultaneously. The experimental results demonstrate that RZW-SR has superior DRM performance compared to existing schemes. First, RZW-SR can obtain the ownership shares relevant to a particular 3D video precisely and reliably for effective copyright identification of large-scale videos. Second, RZW-SR ensures lossless, precise, reliable and flexible copyright identification for 2D frames and depth maps of 3D videos