Proper Measure for Adversarial Robustness
This paper analyzes the problems of adversarial accuracy and adversarial training. We argue that standard adversarial accuracy fails to properly measure the robustness of classifiers. Its definition has a tradeoff with standard accuracy even when we neglect generalization. In order to handle the problems of the standard adversarial accuracy, we introduce a new measure for the robustness of classifiers called genuine adversarial accuracy. It can measure the adversarial robustness of classifiers without trading off accuracy on clean data and accuracy on the adversarially perturbed samples. In addition, it does not favor a model with invariance-based adversarial examples, samples whose predicted classes are unchanged even if the perceptual classes are changed. We prove that a single nearest neighbor (1-NN) classifier is the most robust classifier according to genuine adversarial accuracy for given data and a norm-based distance metric when the class for each data point is unique. Based on this result, we suggest that using poor distance metrics might be one factor for the tradeoff between test accuracy and $l_p$ norm-based test adversarial robustness.
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial RobustnessSimilar Papers 제목 키워드 기반
How to compare adversarial robustness of classifiers from a global perspective
Adversarial robustness of machine learning models has attracted considerable attention over recent years. Adversarial attacks undermine the reliability of and trust in machine learning models, but the construction of mor…
Adversarial RobustnessBIG-bench Machine LearningCertified Adversarial Robustness Under the Bounded Support Set
Deep neural networks (DNNs) have revealed severe vulnerability to adversarial perturbations, beside empirical adversarial training for robustness, the design of provably robust classifiers attracts more and more attentio…
Adversarial RobustnessComplexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness
Quantifying robustness in a single measure for the purposes of model selection, development of adversarial training methods, and anticipating trends has so far been elusive. The simplest metric to consider is the number …
Adversarial RobustnessModel SelectionTargeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking
Deep Reinforcement Learning (RL) agents are susceptible to adversarial noise in their observations that can mislead their policies and decrease their performance. However, an adversary may be interested not only in decre…
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Correlation Analysis between the Robustness of Sparse Neural Networks and their Random Hidden Structural Priors
Deep learning models have been shown to be vulnerable to adversarial attacks. This perception led to analyzing deep learning models not only from the perspective of their performance measures but also their robustness to…