Backdoor Injection

Backdoor injection attacks exploit vulnerabilities in machine learning models by subtly altering training data to trigger malicious behavior under specific, often hidden, input conditions. Current research focuses on developing increasingly sophisticated attacks targeting diverse model architectures, including convolutional neural networks, image-to-image networks, large language models, and diffusion models, often employing techniques like data poisoning, adversarial perturbations, and genetic algorithms to optimize trigger design and stealth. The significance of this research lies in its implications for the security and trustworthiness of AI systems across various applications, from autonomous driving to natural language processing, highlighting the urgent need for robust defense mechanisms.

Papers