arXiv Computer Vision
WaveMark: Robust Wavelet-Domain Image Watermarking for Diffusion Models
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Exploring Weaknesses of Generative Image Watermarks against Latent Frequency Masking
The paper introduces Latent Frequency Masking, an attack that removes or weakens invisible watermarks in AI-generated images by altering selected Fourier coefficients in the latent space. It demonstrates that this method can effectively compromise six diffusion watermarking techniques while maintaining image quality and running faster than existing attacks. The study provides a theoretical distortion bound and evaluates the attack on images from DiffusionDB and MS-COCO prompts.
arXiv AI
Aug 12
MarkNull: Model-Agnostic Watermark Removal in AI-Generated Images via On-Manifold Latent Manipulation
arXiv:2608. 10166v1 Announce Type: cross Abstract: Digital watermarking has emerged as a critical technique for provenance and copyright attribution in AI-generated imagery, yet its robustness against realistic, model-agnostic removal attacks remains poorly explored.
arXiv AI
Oct 1
WARP: A Unified Benchmark for Invisible Image Watermarking -- Robustness and Protection Against Attacks
arXiv:2609.40031v1 Announce Type: cross Abstract: Digital image watermarking is increasingly critical in media contexts, as emerging regulations and industry practices require marking AI-generated co...
arXiv Computer Vision
Sep 28
FeatMark: Feature-level Watermark Protection against Mimicry Attacks with Diffusion Models
FeatMark is a watermarking framework that protects images from text‑to‑image diffusion model mimicry attacks by embedding small, scene‑consistent micro‑features instead of pixel‑level perturbations. It constructs domain‑specific feature banks, selects executable features, and injects them via mask‑guided concept editing to create highly localized, natural edits. Experiments on VGGFace2, CelebA‑HQ, and WikiArt show FeatMark remains robust against ten strong watermark removal attacks and several adaptive attacks, with minimal impact on perceptual quality and extending to video mimicry scenarios.
arXiv AI
Sep 2
One Prompt Is Enough: Watermark Laundering Through Foundation Image Models
The paper introduces the concept of watermark laundering, where an attacker uses a single reconstruction prompt on public foundation image models to produce a visually faithful output that renders invisible watermarks undecodable. The authors evaluate this failure mode across six OpenAI and Google image editing models, three watermarking schemes, and 1,800 reconstructions, finding that OpenAI models cause the strongest payload disruption while Nano Banana 2 shows vulnerability of DwtDct under high-fidelity reconstruction. Prompt ablation experiments reveal that the disruption is driven by the reconstruction pathway itself rather than any specific removal instruction, highlighting prompt-conditioned reconstruction as a distinct attack interface.
Hugging Face Trending Papers
Sep 30
WARP: A Unified Benchmark for Invisible Image Watermarking -- Robustness and Protection Against Attacks
Digital image watermarking is increasingly critical in media contexts, as emerging regulations and industry practices require marking AI-generated content and ensuring traceable sources to prevent man...