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Doctoral Consortium of WSDM'22: Exploring the Bias of Adversarial Defenses

Published: 15 February 2022 Publication History

Abstract

Deep neural networks (DNNs) have achieved extraordinary accomplishments on various machine learning tasks. However, the existence of adversarial attacks still raise great concerns when they are adopted to safety-critical tasks. As countermeasures to protect DNN models against adversarial attacks, there are various defense strategies proposed. However, we find that the robustness ("safety'') provided by the robust training algorithms usually result unequal performance either among classes or sub-populations across the whole data distribution. For example, the model can achieve extremely low accuracy / robustness on certain groups of data. As a result, the safety of the model is still under great threats. As a summary, our project is about to study the bias problems of robust trained neural networks from different perspectives, which aims to build eventually reliable and safe deep learning models. We propose to present our research works in the Doctoral Consortium in WSDM'22 and gain opportunities to share our contribution to the relate problems.

References

[1]
Buolamwini, J., and Gebru, T. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (2018), pp. 77--91.
[2]
Carlini, N., and Wagner, D. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp) (2017), IEEE, pp. 39--57.
[3]
Cohen, J. M., Rosenfeld, E., and Kolter, J. Z. Certified adversarial robustness via randomized smoothing. arXiv preprint arXiv:1902.02918 (2019).
[4]
Goodfellow, I. J., Shlens, J., and Szegedy, C. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014).
[5]
He, H., and Garcia, E. A. Learning from imbalanced data. IEEE Transactions on knowledge and data engineering 21, 9 (2009), 1263--1284.
[6]
Jin, W., Li, Y., Xu, H., Wang, Y., and Tang, J. Adversarial attacks and defenses on graphs: A review and empirical study. arXiv preprint arXiv:2003.00653 (2020).
[7]
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (2017), pp. 2980--2988.
[8]
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017).
[9]
Nanda, V., Dooley, S., Singla, S., Feizi, S., and Dickerson, J. P. Fairness through robustness: Investigating robustness disparity in deep learning. arXiv preprint arXiv:2006.12621 (2020).
[10]
Shafahi, A., Najibi, M., Ghiasi, M. A., Xu, Z., Dickerson, J., Studer, C., Davis, L. S., Taylor, G., and Goldstein, T. Adversarial training for free! In Advances in Neural Information Processing Systems (2019), pp. 3358--3369.
[11]
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013).
[12]
Wang, Y.-X., Ramanan, D., and Hebert, M. Learning to model the tail. In Advances in Neural Information Processing Systems (2017), pp. 7029--7039.
[13]
Wong, E., and Kolter, Z. Provable defenses against adversarial examples via the convex outer adversarial polytope. In International Conference on Machine Learning (2018), PMLR, pp. 5286--5295.
[14]
Xu, H., Ma, Y., Liu, H., Deb, D., Liu, H., Tang, J., and Jain, A. Adversarial attacks and defenses in images, graphs and text: A review. arXiv preprint arXiv:1909.08072 (2019).
[15]
Zafar, M. B., Valera, I., Gomez Rodriguez, M., and Gummadi, K. P. Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th international conference on world wide web (2017), pp. 1171--1180.
[16]
Zhang, D., Zhang, T., Lu, Y., Zhu, Z., and Dong, B. You only propagate once: Painless adversarial training using maximal principle. arXiv preprint arXiv:1905.00877 2, 3 (2019).
[17]
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., and Jordan, M. I. Theoretically principled trade-off between robustness and accuracy. arXiv preprint arXiv:1901.08573 (2019).

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    cover image ACM Conferences
    WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
    February 2022
    1690 pages
    ISBN:9781450391320
    DOI:10.1145/3488560
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    Published: 15 February 2022

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    1. adversarial attack
    2. deep learning
    3. defense

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