标题： 基于可逆图像变换的可逆对抗样本 显示英文标题

标题： Reversible Adversarial Example based on Reversible Image Transformation

摘要： 目前有许多公司使用最先进的深度神经网络（DNNs）来分类和分析我们上传到社交网络或云上的照片。为了防止用户隐私泄露，可以利用对抗样本的攻击特征使这些模型误判。在本文中，我们利用可逆图像变换构建可逆对抗样本，这仍然是对DNNs的对抗样本。它不仅允许DNNs提取错误的信息，而且可以恢复到其原始图像而没有任何失真。实验结果表明，通过我们的方法获得的可逆对抗样本在确保可逆图像质量仍然较高的情况下，具有更高的攻击成功率。此外，所提出的方法易于操作，适合实际应用。 显示英文摘要

摘要： At present there are many companies that take the most advanced Deep Neural Networks (DNNs) to classify and analyze photos we upload to social networks or the cloud. In order to prevent users privacy from leakage, the attack characteristics of the adversarial example can be exploited to make these models misjudged. In this paper, we take advantage of reversible image transformation to construct reversible adversarial example, which is still an adversarial example to DNNs. It not only allows DNNs to extract the wrong information, but also can be recovered to its original image without any distortion. Experimental results show that reversible adversarial examples obtained by our method have higher attack success rates while ensuring that the reversible image quality is still high. Moreover, the proposed method is easy to operate, suitable for practical applications.