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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Andrew Ross

Harvard University


Finale Doshi-Velez

Harvard University


DOI:

10.1609/aaai.v32i1.11504


Abstract:

Deep neural networks have proven remarkably effective at solving many classification problems, but have been criticized recently for two major weaknesses: the reasons behind their predictions are uninterpretable, and the predictions themselves can often be fooled by small adversarial perturbations. These problems pose major obstacles for the adoption of neural networks in domains that require security or transparency. In this work, we evaluate the effectiveness of defenses that differentiably penalize the degree to which small changes in inputs can alter model predictions. Across multiple attacks, architectures, defenses, and datasets, we find that neural networks trained with this input gradient regularization exhibit robustness to transferred adversarial examples generated to fool all of the other models. We also find that adversarial examples generated to fool gradient-regularized models fool all other models equally well, and actually lead to more "legitimate," interpretable misclassifications as rated by people (which we confirm in a human subject experiment). Finally, we demonstrate that regularizing input gradients makes them more naturally interpretable as rationales for model predictions. We conclude by discussing this relationship between interpretability and robustness in deep neural networks.

Topics: AAAI

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Andrew Ross||Finale Doshi-Velez Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Andrew Ross||Finale Doshi-Velez Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients AAAI 2018, .

Andrew Ross||Finale Doshi-Velez (2018). Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Andrew Ross||Finale Doshi-Velez. Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Andrew Ross||Finale Doshi-Velez. 2018. Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Andrew Ross||Finale Doshi-Velez. (2018) "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Andrew Ross||Finale Doshi-Velez, "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients", AAAI, p., 2018.

Andrew Ross||Finale Doshi-Velez. "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Andrew Ross||Finale Doshi-Velez. "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Andrew Ross||Finale Doshi-Velez. Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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