{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/improving-the-adversarial-robustness-and","title":"Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients","arxiv_id":"1711.09404","date":"2017-11-26","proceeding":null,"authors":["Andrew Slavin Ross","Finale Doshi-Velez"],"abstract":"Deep neural networks have proven remarkably effective at solving many\nclassification problems, but have been criticized recently for two major\nweaknesses: the reasons behind their predictions are uninterpretable, and the\npredictions themselves can often be fooled by small adversarial perturbations.\nThese problems pose major obstacles for the adoption of neural networks in\ndomains that require security or transparency. In this work, we evaluate the\neffectiveness of defenses that differentiably penalize the degree to which\nsmall changes in inputs can alter model predictions. Across multiple attacks,\narchitectures, defenses, and datasets, we find that neural networks trained\nwith this input gradient regularization exhibit robustness to transferred\nadversarial examples generated to fool all of the other models. We also find\nthat adversarial examples generated to fool gradient-regularized models fool\nall other models equally well, and actually lead to more \"legitimate,\"\ninterpretable misclassifications as rated by people (which we confirm in a\nhuman subject experiment). Finally, we demonstrate that regularizing input\ngradients makes them more naturally interpretable as rationales for model\npredictions. We conclude by discussing this relationship between\ninterpretability and robustness in deep neural networks.","url_abs":"http://arxiv.org/abs/1711.09404v1","url_pdf":"http://arxiv.org/pdf/1711.09404v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"improving-the-adversarial-robustness-and","repo_url":"https://github.com/dtak/adversarial-robustness-public","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"adversarial-robustness","task_name":"Adversarial Robustness"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.09404","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}