WaveMark is a novel wavelet-domain watermarking method that embeds messages in a diffusion model's initial noise to support robust recovery from perturbed carriers, and distributes keyed replicas of message bits across spatial positions, wavelet orientations, and scales.
Abstract
Image watermarking enables provenance tracing for photorealistic images generated by diffusion models. However, state-of-the-art (SOTA) removal attacks can suppress embedded signals with little perceptual degradation. This vulnerability limits the effectiveness of watermarks for intellectual property protection and misuse attribution. We propose WaveMark, a novel wavelet-domain watermarking method that embeds messages in a diffusion model's initial noise. To support robust recovery from perturbed carriers, WaveMark distributes keyed replicas of message bits across spatial positions, wavelet orientations, and scales. Each carrier groups coefficients from four latent channels and encodes a randomized bit through magnitude projection, providing an explicit decoding margin. We evaluate WaveMark with SD2.1 on SDP and MS-COCO Captions under 14 attacks. WaveMark achieves 100.0% bit accuracy (BA) without attacks, while maintaining image quality comparable to the evaluated baselines. Under SOTA MarkNull and MarkNull-A, it retains over 88% BA, outperforming all baselines.
The proliferation of artificial intelligence-generated content (AIGC) has greatly boosted image synthesis efficiency, yet it also raises serious concerns over copyright infringement and content forgery. Existing watermarking methods generally show weak resistance to diffusion-based regeneration attacks, and struggle to...
Zhi-Tao Han, Siau-Chuin Liew, Anis Farihan Mat Raffei et al.· 2026 IEEE 1st International...· 0 citations
Watermarking diffusion-generated images requires balancing provenance signals with image quality, robustness, and computational cost. This work organizes methods along two axes: insertion mechanism and primary signal-bearing representation, and formalizes a representative $z_T$-Fourier pipeline for verification and ide...
The proliferation of high-fidelity generative editing models has made it possible to inject violent or sexual content into otherwise ordinary images while preserving visual plausibility, with concrete consequences for public discourse and vulnerable populations. We propose a robust semantic watermarking framework that...
Disentangle and Drop (DnD) is proposed, an attack that is agnostic to the watermark method and treats watermark removal as a representation routing problem and shows that one shared operating setting gives competitive removal with high visual fidelity.
Qi Li, Ji-Dong Yang, Feng-Lei Fan et al.· 0 citations
This work identifies two complementary laundering regimes: OpenAI models produce the strongest payload disruption across the evaluated schemes, whereas Nano Banana 2 shows that DwtDct remains vulnerable under high-fidelity reconstruction.
Invisible watermarking has become a central tool for tracing AI-generated images, but its robustness against adaptive removal attacks remains an open security question. We introduce Latent Frequency Masking, an attack that erases watermark evidence by replacing selected Fourier coefficients in the latent representation...
Kirill Aistov, Khaled Abud, I. Serzhenko et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.