11 citations · 11 across the 2 of their papers we have counts for
2 papers
cs.CR2023★ 11 cited
Adversarial Machine Learning and Cybersecurity: Risks, Challenges, and Legal Implications
Micah Musser, Andrew Lohn, James X. Dempsey +14
In July 2022, the Center for Security and Emerging Technology (CSET) at Georgetown University and the Program on Geopolitics, Technology, and Governance at the Stanford Cyber Polic…
cs.LG2021
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks
Marissa Dotter, Sherry Xie, Keith Manville +3
Machine Learning (ML) models are known to be vulnerable to adversarial inputs and researchers have demonstrated that even production systems, such as self-driving cars and ML-as-a-…