Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Understand

Deep learning models have achieved high performance on many tasks, and thus have been applied to many security-critical scenarios.

• For example, deep learning-based face recognition systems have been used to authenticate users to access many security-sensitive applications like payment apps.
• Such usages of deep learning systems provide the adversaries with sufficient incentives to perform attacks against these systems for their adversarial purposes.
• In this work, we consider a new type of attacks, called backdoor attacks, where the attacker's goal is to create a backdoor into a learning-based authentication system, so that he can easily circumvent the system by leveraging the backdoor.