  Adding perturbations to images can mislead classification models to produce
incorrect results. Recently, researchers exploited adversarial perturbations to
protect image privacy from retrieval by intelligent models. However, adding
adversarial perturbations to images destroys the original data, making images
useless in digital forensics and other fields. To prevent illegal or
unauthorized access to sensitive image data such as human faces without
impeding legitimate users, the use of reversible adversarial attack techniques
is increasing. The original image can be recovered from its reversible
adversarial examples. However, existing reversible adversarial attack methods
are designed for traditional imperceptible adversarial perturbations and ignore
the local visible adversarial perturbation. In this paper, we propose a new
method for generating reversible adversarial examples based on local visible
adversarial perturbation. The information needed for image recovery is embedded
into the area beyond the adversarial patch by the reversible data hiding
technique. To reduce image distortion, lossless compression and the B-R-G
(bluered-green) embedding principle are adopted. Experiments on CIFAR-10 and
ImageNet datasets show that the proposed method can restore the original images
error-free while ensuring good attack performance.
