On the Detection of Adversarial Attacks against Deep Neural Networks
Weiyu Wang, Quanyan Zhu · 2017
Deep learning model has been widely studied and proven to achieve high accuracy in various pattern recognition tasks, especially in image recognition. However, due to its non-linear architecture and high-dimensional inputs, its ill-posedness [1] towards adversarial perturbations-small deliberately crafted perturbations on the input will lead to completely different outputs, has also attracted researchers' attention.