  Reversible adversarial examples (RAE) combine adversarial attacks and
reversible data-hiding technology on a single image to prevent illegal access.
Most RAE studies focus on achieving white-box attacks. In this paper, we
propose a novel framework to generate reversible adversarial examples, which
combines a novel beam search based black-box attack and reversible data hiding
with grayscale invariance (RDH-GI). This RAE uses beam search to evaluate the
adversarial gain of historical perturbations and guide adversarial
perturbations. After the adversarial examples are generated, the framework
RDH-GI embeds the secret data that can be recovered losslessly. Experimental
results show that our method can achieve an average Peak Signal-to-Noise Ratio
(PSNR) of at least 40dB compared to source images with limited query budgets.
Our method can also achieve a targeted black-box reversible adversarial attack
for the first time.
