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 an
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
Cihang Xie
Xinyun Chen's Homepage
Yingwei Li
Homepage of Xianglong Liu, Beihang University
Quanshi Zhang | 张拳石
Aniruddha Saha
Haotong Qin
Yu-Lun Liu
ICCV23 AROW Workshop
Xiaogeng Liu - Xiaogeng Liu