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1st International Workshop on Adversarial
Learning for Multimedia

Workshop at ACM Multimedia 2021

Overview

Deep learning has achieved significant success in multimedia fields involving computer vision, natural language processing, and acoustics. However research in adversarial learning also shows that they are highly vulnerable to adversarial examples. Extensive works have demonstrated that adversarial examples could easily fool deep neural networks to wrong predictions threatening practical deep learning applications in both digital and physical world. Though challenging, discovering and harnessing adversarial attacks is beneficial for diagnosing model blind-spots and further understanding as well as improving multimedia systems in practice.

In this workshop, we aim to bring together researchers from the fields of adversarial machine learning, model robustness, and explainable AI to discuss recent research and future directions for adversarial robustness of deep learning models, with a particular focus on multimedia applications, including computer vision, acoustics, etc. As far as we know, we are the first workshop to focus on adversarial learning of multimedia deep learning systems, which is of great significance.

Important Dates

Workshop Schedule

Keynote Speakers

Alan Yuille

Johns Hopkins University

Xiaochun Cao

Chinese Academy of Sciences

Bo Li

University of Illinois at Urbana-Champaign

Tom Goldstein

University of Maryland

Baoyuan Wu

The Chinese University of HongKong, Shenzhen

Pin-Yu Chen

IBM

Boqing Gong

Google

Cihang Xie

University of California, Santa Cruz

Call for Papers

Deep learning has achieved significant success in multimedia fields, however research in adversarial learning also shows that it is highly vulnerable to adversarial examples. We invite submissions on any aspect of adversarial machine learning in multimedia deep learning systems. Topics include but not limited to:

• Adversarial attacking deep learning systems
• Robust architectures against adversarial attacks
• Training techniques for building robust deep learning systems
• Benchmark for evaluating model robustness
• Understanding the adversarial vulnerabilities of deep learning systems
• Improving generalization performance of computer vision systems to out-of-distribution samples
• Explainable AI

Organizers

Dawn Song

UC Berkeley

Dacheng Tao

JD Explore Academy

Alan Yuille

Johns Hopkins University

Anima Anandkumar

California Institute of Technology

Xianglong Liu

Beihang University

Aishan Liu

Beihang University

Xinyun Chen

UC Berkeley

Yingwei Li

Johns Hopkins University

Chaowei Xiao

NVIDIA Research & Arizona State University

Xun Yang

National University of Singapore

Paper Submission

Format: Submitted papers (.pdf format) must use the ACM Article Template https://www.acm.org/publications/proceedings-template. Please remember to add Concepts and Keywords.
Length: As stated in the CfP, submitted papers may be 6 to 8 pages. Up to two additional pages may be added for references. The reference pages must only contain references. Overlength papers will be rejected without review.
Submission Site: https://cmt3.research.microsoft.com/AdvM2021 .

Program Committee

• Jiakai Wang, Beihang University
• Ruihao Gong, SenseTime
• Xiaohui Zeng, University of Toronto
• Renshuai Tao, Beihang University
• Zhuozhuo Tu, the University of Sydney

• Tianlin Li, Nanyang Technological University
• Yuqing Ma, Beihang University
• Huiyuan Xie, University of Cambridge
• Shiyu Tang, Beihang University
• Bo Sun, UT Austin

TBD

Related Workshops

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Adversarial Robustness in the Real World (Workshop at ICCV 2021)

Uncertainty & Robustness in Deep Learning (Workshop at ICML 2021)

Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems and Online Challenges (Workshop at CVPR 2021)

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