1. Addepalli, S., Vivek, B.S., Baburaj, A., Sriramanan, G., Babu, R.V.: Towards achieving adversarial robustness by enforcing feature consistency across bit planes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020
2. Biggio, B., et al.: Evasion attacks against machine learning at test time. CoRR abs/1708.06131 (2017). http://arxiv.org/abs/1708.06131
3. Biggio, B., Roli, F.: Wild patterns: ten years after the rise of adversarial machine learning. CoRR abs/1712.03141 (2017). http://arxiv.org/abs/1712.03141
4. Cai, H., Gan, C., Wang, T., Zhang, Z., Han, S.: Once for all: train one network and specialize it for efficient deployment. In: International Conference on Learning Representations (2020). https://arxiv.org/pdf/1908.09791.pdf
5. Canady, R., Zhou, X., Barve, Y., Balasubramanian, D., Gokhale, A.: Adversarially robust edge-based object detection for assuredly autonomous systems. In: 2022 IEEE International Conference on Assured Autonomy (ICAA), pp. 97–106 (2022). https://doi.org/10.1109/ICAA52185.2022.00021