Fooling AI with AI: An accelerator for adversarial attacks on deep learning visual classification
Document Type
Conference Proceeding
Publication Date
7-1-2019
Abstract
Recent studies identify that Deep learning Neural Networks (DNNs) are vulnerable to subtle perturbations, which are not perceptible to the human visual system but can fool the DNN models and lead to wrong outputs. These algorithms are the first efforts to move forward to secure deep learning by providing an avenue to train future defense networks. We propose the first hardware accelerator for adversarial attacks based on memristor crossbar arrays. Our design significantly improves the throughput of a visual adversarial perturbation system, which can further improve the robustness and security of future deep learning systems. Based on the algorithm uniqueness, we propose four implementations for the adversarial attack accelerator (A^3) to improve the throughput, energy efficiency, and computational efficiency.
Publication Source (Journal or Book title)
Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors
First Page
136
Recommended Citation
Guo, H., Peng, L., Zhang, J., Qi, F., & Duan, L. (2019). Fooling AI with AI: An accelerator for adversarial attacks on deep learning visual classification. Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors, 2019-July, 136. https://doi.org/10.1109/ASAP.2019.00-16
