Adversarial Attacks on Deep Neural Networks: an Overview

In 2014, a group of researchers at Google and NYU found that it was far too easy to fool ConvNets with an imperceivable, but carefully constructed nudge in the input [1]. Let's look at an example. We start with an image of a panda, which our neural network correctly recognizes as a "panda" with 57.7% confidence. Add a little bit of carefully constructed noise and the same neural network now thinks this is an image of a gibbon with 99.3% confidence! This is, clearly, an optical illusion -- but for the neural network.