Physical Adversarial Attacks in Simulated Environments

Adversarial attacks against machine learning algorithms are increasingly a threat as object detection, tracking, and identification systems are more frequently and widely deployed in the real world. Current research on the evaluation of adversarial physical attacks and defenses utilize open and static datasets, such as APRICOT, to provide a benchmark for comparison. Evaluating defenses in the real...