Toward Transferable Defense Against Malicious Image Edits

Summary

Researchers propose TDAE, a bimodal framework that adds imperceptible perturbations to input images to resist malicious edits by diffusion-based image editing systems. It combines FlatGrad Defense Mechanism (FDM), which uses gradient regularization to steer perturbations toward flat minima for robustness against unseen editing models, and Dynamic Prompt Defense (DPD), which refines text embeddings to align immunized and original editing outcomes. The authors report state-of-the-art mitigation of malicious edits in intra- and cross-model evaluations.

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