Inversion-based watermarking embeds watermark payloads directly into the generative process, avoiding a separate post-hoc image-domain embedding stage while preserving the native visual fidelity of synthesized images. However, existing methods remain vulnerable to compound lossy post-processing, particularly when rotation is involved, as it disrupts the spatial correspondence required for latent-space decoding. To overcome this limitation, we introduce AnchorMark, a training-free, robust inversion-based watermarking. We uncover a latent-space property termed Rotation Synchrony: image-domain rotations and their counterparts in the recovered initial latent share the same angle. Building on this property, AnchorMark embeds a synchronization anchor in the central region of the initial latent, enabling accurate estimation and correction of the rotation angle during extraction. Experiments show that AnchorMark substantially improves bit accuracy under rotation and combined attacks, with limited impact on image quality.
Yuqi Qian, Yun Cao, Haocheng Fu et al.· 0 citations
Diffusion-based generative image steganography enables covert communication by synthesizing stego images without relying on cover images. However, existing latent-space methods still struggle to balance robustness, steganographic security, and visual fidelity, especially under practical channel distortions such as compression, blur, resizing, and noise. To address these challenges, we propose RIS-MoE, a robust and secure latent-space image steganography framework that integrates distortion-tolerant message representation with receiver-side adaptive latent restoration. At the sender side, a learnable orthogonal transformation converts the secret message into a distributed representation, which is embedded into the diffusion latent through a residual-guided Hide Network. At the receiver side, a plug-and-play Mixture-of-Experts (MoE) denoising module estimates the distortion composition and adaptively fuses specialized restoration experts before message extraction. Extensive experiments show that RIS-MoE achieves strong robustness under single, mixed, and real-world distortions. It maintains extraction accuracy above 90% under all evaluated simulated combined distortions and achieves 94.62% and 95.29% extraction accuracy after real-world Weibo and Instagram transmission, respectively. RIS-MoE also achieves competitive empirical resistance against spatial-domain, latent-domain, and diffusion-aware steganalyzers, while maintaining favorable visual quality with an FID of 7.35 and an LPIPS of 0.21 on Flickr8K. In addition, the proposed MoE module consistently improves representative latent-space steganography pipelines as a plug-and-play restoration component, demonstrating its transferability. The source code is publicly available at: https://github.com/angle-cell/RIS_MOE.
Gen-Fan Yang, Rong-Chang Duan, Hong Zhang et al.· Cybersecurity· 0 citations
