论文标题
对GAN检测的共发生特征的对抗性攻击
Adversarial Attacks on Co-Occurrence Features for GAN Detection
论文作者
论文摘要
生成对抗网络（GAN）的改进大大减少了产生具有独特语义含义的新的，现实的图像的困难。随着产生虚假图像的能力的增长，需要检测到它们。尽管已经为此任务开发了许多方法，但其中大多数仍然容易受到对抗攻击的影响。在本文中，对基于共发生的GAN探测器产生了两次新颖的对抗性攻击。这些是针对这种检测器提出的第一次攻击。我们表明，我们的方法可以将准确性从98％降低到不到4％，而对深度学习模型或权重不了解。此外，借助深度学习模型细节的全面了解，准确性可以降低至0％。
Improvements in Generative Adversarial Networks (GANs) have greatly reduced the difficulty of producing new, photo-realistic images with unique semantic meaning. With this rise in ability to generate fake images comes demand to detect them. While numerous methods have been developed for this task, the majority of them remain vulnerable to adversarial attacks. In this paper, develop two novel adversarial attacks on co-occurrence based GAN detectors. These are the first attacks to be presented against such a detector. We show that our method can reduce accuracy from over 98% to less than 4%, with no knowledge of the deep learning model or weights. Furthermore, accuracy can be reduced to 0% with full knowledge of the deep learning model details.
