论文标题
对抗性彩色电影：有效的物理世界攻击DNNS
Adversarial Color Film: Effective Physical-World Attack to DNNs
论文作者
论文摘要
众所周知，深神经网络（DNN）的性能容易受到微妙的干扰。到目前为止，基于相机的身体对抗攻击并没有引起太多关注，但这是物理攻击的空缺。在本文中，我们提出了一种称为“对抗色膜（ADVCF）”的简单有效的基于相机的物理攻击，该攻击操纵了彩色膜的物理参数以执行攻击。精心设计的实验显示了所提出方法在数字和物理环境中的有效性。此外，实验结果表明，ADVCF生成的对抗样本在攻击传递性方面具有出色的性能，这可以使ADVCF有效的黑盒攻击。同时，我们通过对抗训练给予对ADVCF的防御指导。最后，我们调查了AdvCF对基于视觉的系统的威胁，并为基于摄像机的物理攻击提出了一些有希望的心态。
It is well known that the performance of deep neural networks (DNNs) is susceptible to subtle interference. So far, camera-based physical adversarial attacks haven't gotten much attention, but it is the vacancy of physical attack. In this paper, we propose a simple and efficient camera-based physical attack called Adversarial Color Film (AdvCF), which manipulates the physical parameters of color film to perform attacks. Carefully designed experiments show the effectiveness of the proposed method in both digital and physical environments. In addition, experimental results show that the adversarial samples generated by AdvCF have excellent performance in attack transferability, which enables AdvCF effective black-box attacks. At the same time, we give the guidance of defense against AdvCF by means of adversarial training. Finally, we look into AdvCF's threat to future vision-based systems and propose some promising mentality for camera-based physical attacks.
