Malicious intelligent algorithms greatly threaten the security of social users' privacy by detecting and analyzing the uploaded photos to social network platforms. The destruction to DNNs brought by the adversarial attack sparks the potential that adversarial examples serve as a new protection mechanism for privacy security in social networks. However, the existing adversarial example does not have recoverability for serving as an effective protection mechanism. To address this issue, we propose a recoverable generative adversarial network to generate self-recoverable adversarial examples. By modeling the adversarial attack and recovery as a united task, our method can minimize the error of the recovered examples while maximizing the attack ability, resulting in better recoverability of adversarial examples. To further boost the recoverability of these examples, we exploit a dimension reducer to optimize the distribution of adversarial perturbation. The experimental results prove that the adversarial examples generated by the proposed method present superior recoverability, attack ability, and robustness on different datasets and network architectures, which ensure its effectiveness as a protection mechanism in social networks.
翻译:对抗性攻击对DNNs造成的破坏引发了对抗性例子作为社会网络隐私安全的新保护机制的可能性。然而,现有的对抗性例子不能恢复作为有效保护机制的作用。为解决这一问题,我们提议建立一个可恢复的基因化对抗性网络,以产生可自我恢复的对抗性实例。通过将对抗性攻击和复原作为统一任务进行模拟,我们的方法可以最大限度地减少已追回例子的错误,同时最大限度地扩大攻击能力,从而更好地恢复对抗性例子。为了进一步增强这些例子的可恢复性,我们利用一个维度来优化对抗性扰动的分布。实验结果证明,拟议方法产生的对抗性例子体现了优越的可恢复性、攻击能力以及不同数据集和网络结构的稳健性,从而确保其作为社会网络的保护机制的有效性。