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In the era where AI-generated content (AIGC) models can produce stunning andlifelike images, the lingering shadow of unauthorized reproductions andmalicious tampering poses imminent threats to copyright integrity andinformation security. Current image watermarking methods, while widely acceptedfor safeguarding visual content, can only protect copyright and ensuretraceability. They fall short in localizing increasingly realistic imagetampering, potentially leading to trust crises, privacy violations, and legaldisputes. To solve this challenge, we propose an innovative proactive forensicsframework EditGuard, to unify copyright protection and tamper-agnosticlocalization, especially for AIGC-based editing methods. It can offer ameticulous embedding of imperceptible watermarks and precise decoding oftampered areas and copyright information. Leveraging our observed fragility andlocality of image-into-image steganography, the realization of EditGuard can beconverted into a united image-bit steganography issue, thus completelydecoupling the training process from the tampering types. Extensive experimentsdemonstrate that our EditGuard balances the tamper localization accuracy,copyright recovery precision, and generalizability to various AIGC-basedtampering methods, especially for image forgery that is difficult for the nakedeye to detect. The project page is available athttps://xuanyuzhang21.github.io/project/editguard/.

EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection