Reversible Attack based on Local Visual Adversarial Perturbation
This work addresses privacy protection for sensitive images like human faces, enabling reversible adversarial attacks to prevent unauthorized model access without destroying data for forensic use, though it is incremental as it builds on existing reversible methods by focusing on local visible perturbations.
The paper tackles the problem of protecting sensitive image data from unauthorized access by models while preserving the ability to recover the original images for legitimate use, proposing a reversible adversarial attack method based on local visible perturbations that achieves error-free image restoration and good attack performance on CIFAR-10 and ImageNet datasets.
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.