Selective Amnesia - On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models

Selective Amnesia - On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models

Explore a 15-minute IEEE conference talk on efficient, high-fidelity, and blind suppression of backdoor effects in trojaned machine learning models. Delve into the concept of "Selective Amnesia" presented by researchers from Indiana University Bloomington, including Rui Zhu, Di Tang, Siyuan Tang, XiaoFeng Wang, and Haixu Tang. Learn about innovative techniques to mitigate backdoor vulnerabilities in ML models while maintaining their performance and functionality.