49 citations · 116 across the 9 of their papers we have counts for
11 papers
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…
Boosting Exploration in Multi-Task Reinforcement Learning using Adversarial Networks
Ramnath Kumar, Tristan Deleu, Yoshua Bengio
Advancements in reinforcement learning (RL) have been remarkable in recent years. However, the limitations of traditional training methods have become increasingly evident, particu…
Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks
Kendra Albert, Maggie Delano, Bogdan Kulynych +1
Attacks from adversarial machine learning (ML) have the potential to be used "for good": they can be used to run counter to the existing power structures within ML, creating breath…
Ethical Testing in the Real World: Evaluating Physical Testing of Adversarial Machine Learning
Kendra Albert, Maggie Delano, Jonathon Penney +2
This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems…
Legal Risks of Adversarial Machine Learning Research
Ram Shankar Siva Kumar, Jonathon Penney, Bruce Schneier +1
Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML systems such as those used in Facebook, Tesla, Microsoft, IBM, Google to demonstrat…
Politics of Adversarial Machine Learning
Kendra Albert, Jonathon Penney, Bruce Schneier +1
In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subject…