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It is significant to evaluate the security of existing digital imagetampering localization algorithms in real-world applications. In this paper, wepropose an adversarial attack scheme to reveal the reliability of suchtampering localizers, which would be fooled and fail to predict altered regionscorrectly. Specifically, the adversarial examples based on optimization andgradient are implemented for white/black-box attacks. Correspondingly, theadversarial example is optimized via reverse gradient propagation, and theperturbation is added adaptively in the direction of gradient rising. Theblack-box attack is achieved by relying on the transferability of suchadversarial examples to different localizers. Extensive evaluations verify thatthe proposed attack sharply reduces the localization accuracy while preservinghigh visual quality of the attacked images.

Transferable Adversarial Attack on Image Tampering Localization