Title: CAN MACHINE TELL THE DISTORTION DIFFERENCE? A REVERSE ENGINEERING STUDY OF ADVERSARIAL ATTACKS. Abstract: Deep neural networks have achieved remarkable performance in many areas, including image-related classification tasks. However, various studies have shown that they are vulnerable to adversarial examples – images that are carefully crafted to fool well-trained deep neural networks by introducing imperceptible perturbations to the original images. To better understand the inherent characteristics of adversarial attacks, we study the features of three common attack families: gradient-based, score-based, and decision-based. In this paper, we demonstrate that given adversarial examples, attacks from different families can be successfully identified with a simple model. To investigate the reason behind it, we further study the perturbation patterns of different attacks with carefully designed experiments. Experimental results on CIFAR10 and Tiny ImageNet confirm the differences of attacks in distortion patterns. 