EditInk: Learning Edit-Sensitive Ink to Make AI Manipulation Legible

Generative image editing can produce visually convincing local modifications, making manipulated regions difficult to identify. Proactive localization addresses this challenge by embedding signals before image release to provide additional localization cues. However, jointly learning a signal and its reader from actual editing outcomes remains challenging when closed-source editors allow forward queries but not backpropagation. To address this challenge, we propose a localization framework named EditInk that learns a shared edit-sensitive ink together with an ink developer. The ink is an image-embedded signal learned to remain imperceptible before editing and provide spatial cues when disrupted, while the developer reads these cues to identify manipulated regions from the edited image alone. Yet training the developer relies on recognizable disruption cues from the ink, while improving the ink relies on reliable localization guidance from the developer. We address this mutual dependence through an alternating cooperative learning strategy that coordinates two complementary phases. In the edit-driven developer learning phase, we hold the current ink fixed and train the developer on actual edits of protected images. In the subsequent developer-guided ink learning phase, we hold the updated developer fixed and optimize the ink using a protected view and differentiable simulated views constructed by local ink erasure followed by image degradation. Repeatedly alternating these phases allows actual editing outcomes to guide ink learning through the developer, without backpropagating through the editors. Experiments on natural image and face datasets show that EditInk achieves higher performance than the compared passive and proactive baselines on both open and closed source editors.