Editing Away the Evidence: Diffusion-Based Image Manipulation and the Failure Modes of Robust Watermarking

TL;DR

This paper analyzes how diffusion-based image editing can unintentionally remove or weaken robust watermarks, challenging their reliability for content protection and proposing principles for more resilient watermarking methods.

Contribution

It provides a unified theoretical and empirical framework to understand the failure modes of watermarking under diffusion editing and offers insights for designing more robust schemes.

Findings

Diffusion editing can significantly degrade watermark signals.

Theoretical bounds show conditions where watermark recovery is impossible.

Routine edits often reduce watermark detectability.

Abstract

Robust invisible watermarks are widely used to support copyright protection, content provenance, and accountability by embedding hidden signals designed to survive common post-processing operations. However, diffusion-based image editing introduces a fundamentally different class of transformations: it injects noise and reconstructs images through a powerful generative prior, often altering semantic content while preserving photorealism. In this paper, we provide a unified theoretical and empirical analysis showing that non-adversarial diffusion editing can unintentionally degrade or remove robust watermarks. We model diffusion editing as a stochastic transformation that progressively contracts off-manifold perturbations, causing the low-amplitude signals used by many watermarking schemes to decay. Our analysis derives bounds on watermark signal-to-noise ratio and mutual information along diffusion trajectories, yielding conditions under which reliable recovery becomes information-theoretically impossible. We further evaluate representative watermarking systems under a range of diffusion-based editing scenarios and strengths. The results indicate that even routine semantic edits can significantly reduce watermark recoverability. Finally, we discuss the implications for content provenance and outline principles for designing watermarking approaches that remain robust under generative image editing.