Revisiting Adversarial Risk

By Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar. 2018

20 citations recorded by the source corpus.

Recent works on adversarial perturbations show that there is an inherent trade-off between standard test accuracy and adversarial accuracy. Specifically, they show that no classifier can simultaneously be robust to adversarial perturbations and achieve high standard test accuracy. However, this is contrary to the standard notion that on tasks such as image classification, humans are robust classifiers with low error rate. In this work, we show that the main reason behind this confusion is the inexact definition of adversarial perturbation that is used in the literature. To fix this issue, we propose a slight, yet important modification to the existing definition of adversarial perturbation. Based on the modified definition, we show that there is no trade-off between adversarial and standard accuracies; there exist classifiers that are robust and achieve high standard accuracy. We further study several properties of this new definition of adversarial risk and its relation to the existing definition.

Subfields: Artificial Intelligence, Hardware and Architecture, Molecular Biology, Safety, Robustness & Interpretability

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Citations in the corpus

Feature Purification: How Adversarial Training Performs Robust Deep Learning (2020, background): “In particular, we found that the following fundamental questions remain largely unaddressed: Questions Why do adversarial examples exist when we train the neural networks using the original training data set? How can adversarial training further “robustify” the trained neural networks against these ”