Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks

no code implementations • 3 Aug 2020 • Haoqiang Guo, Lu Peng, Jian Zhang, Fang Qi, Lide Duan

Recent studies identify that Deep learning Neural Networks (DNNs) are vulnerable to subtle perturbations, which are not perceptible to human visual system but can fool the DNN models and lead to wrong outputs.

Adversarial Attack Computational Efficiency