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引用次数: 0
摘要
在本文中,我们介绍了两种利用像素值多样性检测对抗示例的新方法。首先,我们提出了像素值多样性的概念(它反映了图像中像素值的分布)和两个独立的指标(UPVR 和 RPVR)来分别评估像素值多样性。然后,我们分别提出了基于阈值法和贝叶斯法的两种检测对抗示例的方法。实验结果表明,与优秀的先验方法 LID 相比,我们提出的方法在检测对抗性示例方面取得了更好的性能。我们还展示了我们提出的方法对自适应攻击方法的鲁棒性。
Detecting Adversarial Examples Utilizing Pixel Value Diversity
In this paper, we introduce two novel methods to detect adversarial examples utilizing pixel value diversity. First, we propose the concept of pixel value diversity (which reflects the spread of pixel values in an image) and two independent metrics (UPVR and RPVR) to assess the pixel value diversity separately. Then we propose two methods to detect adversarial examples based on the threshold method and Bayesian method respectively. Experimental results show that compared to an excellent prior method LID, our proposed methods achieve better performances in detecting adversarial examples. We also show the robustness of our proposed work against an adaptive attack method.
期刊介绍:
TODAES is a premier ACM journal in design and automation of electronic systems. It publishes innovative work documenting significant research and development advances on the specification, design, analysis, simulation, testing, and evaluation of electronic systems, emphasizing a computer science/engineering orientation. Both theoretical analysis and practical solutions are welcome.