Breakthroughs in machine learning have resulted in state-of-the-art deep neural networks (DNNs) performing classification tasks in safety-critical applications. Recent research has demonstrated that DNNs can be attacked through adversarial examples, which are small perturbations to input data that cause the DNN to misclassify objects. The proliferation of DNNs raises important safety concerns about designing systems that are robust to adversarial examples. In this work we develop adversarial examples to attack the Yolo V3 object detector [1] and then study strategies to detect and neutralize these examples. Python code for this project is available at https://github.com/ianmcdiarmidsterling/adversarial
@article{arxiv.2008.10106,
title = {Developing and Defeating Adversarial Examples},
author = {Ian McDiarmid-Sterling and Allan Moser},
journal= {arXiv preprint arXiv:2008.10106},
year = {2020}
}