  In the era where AI-generated content (AIGC) models can produce stunning and
lifelike images, the lingering shadow of unauthorized reproductions and
malicious tampering poses imminent threats to copyright integrity and
information security. Current image watermarking methods, while widely accepted
for safeguarding visual content, can only protect copyright and ensure
traceability. They fall short in localizing increasingly realistic image
tampering, potentially leading to trust crises, privacy violations, and legal
disputes. To solve this challenge, we propose an innovative proactive forensics
framework EditGuard, to unify copyright protection and tamper-agnostic
localization, especially for AIGC-based editing methods. It can offer a
meticulous embedding of imperceptible watermarks and precise decoding of
tampered areas and copyright information. Leveraging our observed fragility and
locality of image-into-image steganography, the realization of EditGuard can be
converted into a united image-bit steganography issue, thus completely
decoupling the training process from the tampering types. Extensive experiments
demonstrate that our EditGuard balances the tamper localization accuracy,
copyright recovery precision, and generalizability to various AIGC-based
tampering methods, especially for image forgery that is difficult for the naked
eye to detect. The project page is available at
https://xuanyuzhang21.github.io/project/editguard/.
