  It is significant to evaluate the security of existing digital image
tampering localization algorithms in real-world applications. In this paper, we
propose an adversarial attack scheme to reveal the reliability of such
tampering localizers, which would be fooled and fail to predict altered regions
correctly. Specifically, the adversarial examples based on optimization and
gradient are implemented for white/black-box attacks. Correspondingly, the
adversarial example is optimized via reverse gradient propagation, and the
perturbation is added adaptively in the direction of gradient rising. The
black-box attack is achieved by relying on the transferability of such
adversarial examples to different localizers. Extensive evaluations verify that
the proposed attack sharply reduces the localization accuracy while preserving
high visual quality of the attacked images.
