arXiv Computer Vision
FiLM-CAM: Keyed Feature Modulation for Conditional-Access Watermarking
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arXiv Computer Vision
Sep 1
Asymmetric Phase Coding Video Watermarking
arXiv:2608.29212v1 Announce Type: cross Abstract: Existing video watermarking systems are symmetric: the party that can verify a mark holds the extractor weights or generator secret and can therefore...
arXiv Computer Vision
Sep 22
An Efficient and Effective Watermarking Scheme for the Protection of the Intellectual Property Rights of Video Generative Models
arXiv:2609.23586v1 Announce Type: new Abstract: The rapid development of video generative models (VGMs) has enabled the generation of highly realistic synthetic videos, raising concerns about the int...
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.
Hugging Face Trending Papers
Sep 25
FeatMark: Feature-level Watermark Protection against Mimicry Attacks with Diffusion Models
Text-to-image diffusion models enable data-efficient "mimicry" attacks, wherein adversaries fine-tune the model on a handful of public photos to synthesize convincing forgeries of a target individual....
arXiv Computer Vision
Oct 12
WaveMark: Robust Wavelet-Domain Image Watermarking for Diffusion Models
WaveMark introduces a wavelet‑domain watermarking technique that embeds messages into the initial noise of diffusion models, distributing keyed replicas across spatial positions, wavelet orientations, and scales to enhance robustness. The method encodes each carrier with a randomized bit via magnitude projection of four latent channel coefficients, yielding a clear decoding margin. Experiments on SD2.1 with SDP and MS‑COCO Captions show perfect bit accuracy without attacks and over 88% accuracy against state‑of‑the‑art removal attacks, outperforming existing baselines while preserving image quality.
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...