The Art of Robustness:
Devil and Angel in Adversarial Machine Learning

Deep learning has achieved significant success in multiple fields, including computer vision. However, studies in adversarial machine learning also indicate that deep learning models are highly vulnerable to adversarial examples. Extensive works have demonstrated that adversarial examples are serving as a devil for the robustness of deep neural networks, which threatens the deep learning based applications in both the digital and physical world.

Though harmful, adversarial attacks can be also shown as an angel for deep learning models. Discovering and harnessing adversarial examples properly could be highly beneficial across several domains including improving model robustness, diagnosing model blind spots, protecting data privacy, safety evaluation, and further understanding vision systems in practice. Since there are both the devil and angel roles of adversarial learning, exploring robustness is an art of balancing and embracing both the light and dark sides of adversarial examples.

In this workshop, we aim to bring together researchers from the fields of computer vision, machine learning, and security to jointly cooperate with a series of meaningful works, lectures, and discussions. We will focus on the most recent progress and also the future directions of both the positive and negative aspects of adversarial machine learning, especially in computer vision. Different from the previous workshops on adversarial machine learning, our proposed workshop aims to explore both the devil and angel characters for building trustworthy deep learning models.

Overall, this workshop consists of invited talks from the experts in this area, research paper submissions, and a large-scale online competition on building robust models. In particular, the competition includes two tracks as (1) Building digital robust classifiers on ImageNet and (2) Building physical robust adversarial detectors on open-set.