arXiv AI
LiBRA: Detection-Aware Image Watermark Removal via Bidirectional Latent Optimization
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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 2
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 Computer Vision
1d ago
WaveMark: Robust Wavelet-Domain Image Watermarking for Diffusion Models
arXiv:2610.12952v1 Announce Type: cross Abstract: Image watermarking enables provenance tracing for photorealistic images generated by diffusion models. However, state-of-the-art (SOTA) removal attac...
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...
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...
arXiv Computer Vision
Aug 27
IRIS: Visual-Semantic Binding for Forgery-Resistant Watermarking of Diffusion Images
IRIS is a training‑free watermarking scheme for diffusion‑generated images that binds a watermark to the image’s visual semantics. It derives an intrinsic ring identifier from a content code of the non‑watermarked image and injects it late in the generation trajectory, ensuring the mark survives common processing while breaking under semantic changes or foreign images. Experiments on three prompt datasets show IRIS reliably detects watermarks, maintains fidelity to the original image, and resists forgery techniques that defeat other marks.