Adversarial Example Games

By Avishek Joey Bose, Gauthier Gidel, Hugo Berrard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton. 2020

57 citations recorded by the source corpus.

The existence of adversarial examples capable of fooling trained neural network classifiers calls for a much better understanding of possible attacks to guide the development of safeguards against them. This includes attack methods in the challenging non-interactive blackbox setting, where adversarial attacks are generated without any access, including queries, to the target model. Prior attacks in this setting have relied mainly on algorithmic innovations derived from empirical observations (e.g., that momentum helps), lacking principled transferability guarantees. In this work, we provide a theoretical foundation for crafting transferable adversarial examples to entire hypothesis classes. We introduce Adversarial Example Games (AEG), a framework that models the crafting of adversarial examples as a min-max game between a generator of attacks and a classifier. AEG provides a new way to design adversarial examples by adversarially training a generator and a classifier from a given hypothesis class (e.g., architecture). We prove that this game has an equilibrium, and that the optimal generator is able to craft adversarial examples that can attack any classifier from the corresponding hypothesis class. We demonstrate the efficacy of AEG on the MNIST and CIFAR-10 datasets, outperforming prior state-of-the-art approaches with an average relative improvement of $29.9\%$ and $47.2\%$ against undefended and robust models (Table 2 & 3) respectively.

Subfields: Artificial Intelligence, Safety, Robustness & Interpretability, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. William L. Hamilton
  2. Avishek Joey Bose
  3. Simon Lacoste-Julien
  4. Gauthier Gidel
  5. Yoshua Bengio
  6. Pascal Vincent
  7. Mubarak Shah
  8. Ajmal Saeed Mian

In Safety, Robustness & Interpretability

  1. Avishek Joey Bose
  2. William L. Hamilton
  3. Mubarak Shah
  4. Navid Kardan
  5. Ajmal Saeed Mian
  6. Naveed Akhtar
  7. Simon Lacoste-Julien
  8. Pascal Vincent

In Signal Processing

  1. Avishek Joey Bose
  2. William L. Hamilton
  3. Mubarak Shah
  4. Navid Kardan
  5. Naveed Akhtar
  6. Ajmal Saeed Mian
  7. Pascal Vincent
  8. Gauthier Gidel

Citations in the corpus