Robust Backdoor Attack

Robust backdoor attacks aim to create malicious, yet undetectable, vulnerabilities in machine learning models, primarily by poisoning training data with subtly altered inputs (triggers). Current research focuses on developing increasingly stealthy attacks against various model types, including federated learning, speech recognition systems, and object detectors, often employing techniques like steganography, Bayesian approaches, and diffusion models to generate imperceptible triggers and evade defenses. The significance lies in the potential for widespread compromise of AI systems across diverse applications, highlighting the urgent need for robust defense mechanisms and improved model security.