Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation

Understand

Deep learning models have consistently outperformed traditional machine learning models in various classification tasks, including image classification.

• As such, they have become increasingly prevalent in many real world applications including those where security is of great concern.
• Such popularity, however, may attract attackers to exploit the vulnerabilities of the deployed deep learning models and launch attacks against security-sensitive applications.
• In this paper, we focus on a specific type of data poisoning attack, which we refer to as a {\em backdoor injection attack}.