纹理和形状比型模型的普遍对立强力 (Universal Adversarial Robustness of Texture and Shape-Biased Models)

from arxiv, In Proceedings of the 28th IEEE International Conference on Image Processing (ICIP 2021), code available at: https://github.com/kenny-co/sgd-uap-torch

Increasing shape-bias in deep neural networks has been shown to improve robustness to common corruptions and noise. In this paper we analyze the adversarial robustness of texture and shape-biased models to Universal Adversarial Perturbations (UAPs). We use UAPs to evaluate the robustness of DNN models with varying degrees of shape-based training. We find that shape-biased models do not markedly improve adversarial robustness, and we show that ensembles of texture and shape-biased models can improve universal adversarial robustness while maintaining strong performance.

翻译：深层神经网络中日益增强的形状偏差已证明可以提高对常见腐败和噪音的稳健性。在本文中,我们分析了对通用反逆干扰(UAPs)的纹理和形状偏向模型的对抗性强度。我们使用UAPs评估DNN模型的稳健性,并进行了不同程度的基于形状的培训。我们发现,形状偏向模型并不能明显改善对抗性强度,我们表明,质质和形状偏向模型的组合可以提高通用的对抗性强度,同时保持强劲的性能。