Detection of adversarial attacks via disentangling natural images and perturbations

• Main Authors: Qing, Yuanyuan, Bai, Tao, Liu, Zhuotao, Moulin, Pierre, Wen, Bihan
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Adversarial detection; Representation learning
• Online Access: https://hdl.handle.net/10356/178082
• Institution: Nanyang Technological University

The vulnerability of deep neural networks against adversarial attacks, i.e., imperceptible adversarial perturbations can easily give rise to wrong predictions, poses a huge threat to the security of their real-world deployments. In this paper, a novel Adversarial Detection method via Disentangling N...