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A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning
Hang Su · Yinpeng Dong · Tianyu Pang · Eric Wong · Zico Kolter · Shuo Feng · Bo Li · Henry Liu · Dan Hendrycks · Francesco Croce · Leslie Rice · Tian Tian

Sat Jul 24 04:45 AM -- 02:35 PM (PDT) @
Event URL: https://advml-workshop.github.io/icml2021/ »

Adversarial machine learning is a new gamut of technologies that aim to study the vulnerabilities of ML approaches and detect malicious behaviors in adversarial settings. The adversarial agents can deceive an ML classifier by significantly altering its response with imperceptible perturbations to the inputs. Although it is not to be alarmist, researchers in machine learning are responsible for preempting attacks and building safeguards, especially when the task is critical for information security and human lives. We need to deepen our understanding of machine learning in adversarial environments.

While the negative implications of this nascent technology have been widely discussed, researchers in machine learning are yet to explore their positive opportunities in numerous aspects. The positive impacts of adversarial machine learning are not limited to boost the robustness of ML models but cut across several other domains.

Since there are both positive and negative applications of adversarial machine learning, tackling adversarial learning to its use in the right direction requires a framework to embrace the positives. This workshop aims to bring together researchers and practitioners from various communities (e.g., machine learning, computer security, data privacy, and ethics) to synthesize promising ideas and research directions and foster and strengthen cross-community collaborations on both theoretical studies and practical applications. Different from the previous workshops on adversarial machine learning, our proposed workshop seeks to explore the prospects besides reducing the unintended risks for sophisticated ML models.

This is a one-day workshop, planned with a 10-minute opening, 11 invited keynotes, about 9 contributed talks, 2 poster sessions, and 2 special sessions for panel discussion about the prospects and perils of adversarial machine learning.

The workshop is kindly sponsored by RealAI Inc. and Bosch.

Author Information

Hang Su (Tsinghua University)
Yinpeng Dong (Tsinghua University)
Tianyu Pang (Tsinghua University)
Eric Wong (MIT)
Zico Kolter (Carnegie Mellon University / Bosch Center for AI)
Shuo Feng (University of Michigan)
Bo Li (UIUC)
Bo Li

Dr. Bo Li is an assistant professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. She is the recipient of the IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, AI’s 10 to Watch, NSF CAREER Award, MIT Technology Review TR-35 Award, Dean's Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty Award, Intel Rising Star award, Symantec Research Labs Fellowship, Rising Star Award, Research Awards from Tech companies such as Amazon, Facebook, Intel, IBM, and eBay, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, which is at the intersection of machine learning, security, privacy, and game theory. She has designed several scalable frameworks for trustworthy machine learning and privacy-preserving data publishing. Her work has been featured by major publications and media outlets such as Nature, Wired, Fortune, and New York Times.

Henry Liu (U. of Michigan)
Dan Hendrycks (UC Berkeley)
Francesco Croce (University of Tübingen)
Leslie Rice (Carnegie Mellon University)
Tian Tian (Tsinghua University)

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