Exploring the Orthogonality and Linearity of Backdoor Attacks

Kaiyuan Zhang, Siyuan Cheng, Guangyu Shen, Guanhong Tao, Shengwei An, Anuran Makur, Shiqing Ma, Xiangyu Zhang

Abstract: Backdoor attacks embed an attacker-chosen pattern into inputs to cause model misclassification. This security threat to machine learning has been a long concern. There are a number of defense techniques proposed by the community. Do they work for a large spectrum of attacks?As we argue that they are significant and prevalent in contemporary research, and we conduct a systematic study on 14 attacks and 12 defenses. Our empirical results show that existing defenses often fail on certain attacks. To understand the reason, we study the characteristics of backdoor attacks through theoretical analysis. Particularly, we formulate backdoor poisoning as a continual learning task, and introduce two key properties: orthogonality and linearity. These two characteristics in-depth explain how backdoors are learned by models from a theoretical perspective. This helps to understand the reason behind the failure of various defense techniques. Through our study, we highlight open challenges in defending against backdoor attacks and provide future directions.