ADef: an Iterative Algorithm to Construct Adversarial Deformations

04/20/2018
by   Rima Alaifari, et al.
0

While deep neural networks have proven to be a powerful tool for many recognition and classification tasks, their stability properties are still not well understood. In the past, image classifiers have been shown to be vulnerable to so-called adversarial attacks, which are created by additively perturbing the correctly classified image. In this paper, we propose the ADef algorithm to construct a different kind of adversarial attack created by iteratively applying small deformations to the image, found through a gradient descent step. We demonstrate our results on MNIST with a convolutional neural network and on ImageNet with Inception-v3 and ResNet-101.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset