Malevolent machine learning

Abstract

AI attacks throw light on the nature of deep learning.

References

1. Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A. Adversarial Examples Are Not Bugs, They Are Features ArXiv preprint (2019): https://arxiv.org/abs/1905.02175.
2. Wang, H., Wu, X., Yin, P., and Xing, E.P. High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks ArXiv preprint (2019): https://arxiv.org/abs/1905.13545.
3. Papernot, N., and McDaniel P. Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning ArXiv preprint (2018): https://arxiv.org/abs/1803.04765.
4. Jacobsen, J.H., Behrmannn, J., Carlini N., Tramer, F., and Papernot, N. Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness ICLR 2019 Workshop on Safe ML, New Orleans, Louisiana. https://arxiv.org/abs/1903.10484.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Search methodologies

  • Machine learning

    • Machine learning approaches

      • Neural networks