Ieee Computer Society Emerging Technology Fund Recipient Introduces Machine Learning Cybersecurity Benchmarks

Today, at the virtual Backdoor Attacks and Defenses in Machine Learning (BANDS) workshop during The Eleventh International Conference on Learning Representations (ICLR), participants in the IEEE Trojan Removal Competition presented their findings and success rates at effectively and efficiently mitigating the effects of neural trojans while maintaining high performance. The competition’s winning team from the Harbin Institute of Technology in Shenzhen, with set HZZQ Defense, formulated a highly effective solution, resulting in a 98.14% poisoned accuracy rate and only a 0.12% attack success rate. This competition was established by IEEE CS in 2022 and awarded $25,000 USD to IEEE SCSTC for the “Annual Competition on Emerging Issues of Data Security and Privacy (EDISP).”