Neural Backdoor Detection via Psychometric Unlearning

A new arXiv paper proposes a cybernetic framework for detecting and removing neural backdoors in AI systems. Backdoor attacks, likened to hypnopaedia (subliminal conditioning), allow unauthorized manipulation of machine learning models through hidden triggers. The study introduces a self-aware unlearning mechanism that autonomously detaches a model's behavior from backdoor triggers using reverse engineering and statistical inference. The framework continuously monitors untrustworthy data sources to identify threats. The paper is available under arXiv ID 2410.05284.