Combating Adversarial Attacks with a Barrage of Random Transforms (BaRT) NVIDIA Developer Blog

Wherever you look these days, you can find AI affecting your life in one way or another. Whether it's the Netflix recommendation system or self driving cars, the use of deep learning is becoming ever more prevalent throughout our lives and is starting to make increasingly more crucial decisions. Since AI is becoming ingrained in our lives, you'd expect it to be safe and fool proof, right? The potential exists for bad actors to trick deep learning systems into misinterpreting the input on purpose causing it to give a wrong answer. We present a method for preventing these intentional misclassifications to help maintain trust in complex AI systems. Shortly after Alexnet brought neural networks back into the main stream in 2012 [Krizhevsky et al. 2012 ], people were immediately beginning to find ways to manipulate the fundamental structure of these tools.