Image watermarking method resists attacks on preexisting pictures
Attack-Resilient Multi-Bit Image Watermarking Framework via Latent Coordinate Sign Clamping
Cryptography and Security
Summary
Protecting image ownership is hard because methods often can't handle existing images, store many bits of data, or resist deliberate tampering. The authors created a watermarking method that can hide multi-bit messages in any image without needing retraining, and still keep the image looking natural. Their method also includes advanced ways to decode the message even if noise or attacks try to remove it. It works well on natural and AI-generated images and can detect attempts to forge or alter the watermarks.
What this means in practice
- •For digital content distributors: Embed robust multi-bit watermarks into preexisting images to protect copyright against removal and forgery in distribution pipelines.
- •For cloud storage providers: Use watermarking with built-in forgery detection to verify image authenticity after transformations or malicious attacks in cloud services.
- •For video game asset managers: Protect game images and AI-generated graphics from unauthorized use by inserting resilient watermarks that survive modifications.$Commercial implications: Enables selling watermark solutions that secure game assets against piracy, leveraging this paper’s attack-resistant watermarking framework.
Authors
Marjan Beheshti, Ronghua Xu
Abstract
Watermarking is essential for image copyright protection and attribution, yet existing methods suffer from fundamental trade-offs among post-hoc applicability to pre-existing images, multi-bit capacity, training-free deployment, and adversarial resilience. To address these limitations, we propose a training-free watermarking framework that exploits the overcompleteness of diffusion latent spaces to embed multi-bit payloads into arbitrary images while preserving high visual quality. Additionally, we develop a three-layer decoding pipeline that combines magnitude-weighted majority voting, BCH, and Chase decoding to maximize message-extraction accuracy in the presence of channel noise. To counter active adversaries, our framework integrates a steganalysis-resistant noise camouflage layer and a dual-layer forgery defense that combines perceptual hashing with a Reed-Solomon fuzzy commitment scheme over CLIP features. Empirical evaluations demonstrate that our method applies to both natural and AI-generated images with a scalable $32\text{--}128$-bit capacity. Furthermore, it effectively mitigates advanced removal vectors ($94.6\%$ decode rate against CtrlRegen+ regeneration attacks) and secures robust forgery detection ($98.8\%$ accuracy via joint CLIP and RS fuzzy commitment mechanism).