CVPR 2020 Workshop on

Adversarial Machine Learning in Computer Vision

Seattle, Washington
8:30 AM - 7:00 PM on June 19, 2020

[Video Recording]

Our internal conference page at CVPR is here


Overview

Although computer vision models have achieved advanced performance on various recognition tasks in recent years, they are known to be vulnerable against adversarial examples. The existence of adversarial examples reveals that current computer vision models perform differently with the human vision system, and on the other hand provides opportunities for understanding and improving these models.

In this workshop, we will focus on recent research and future directions on adversarial machine learning in computer vision. We aim to bring experts from the computer vision, machine learning and security communities together to highlight the recent progress in this area, as well as discuss the benefits of integrating recent progress in adversarial machine learning into general computer vision tasks. Specifically, we seek to study adversarial machine learning not only for enhancing the model robustness against adversarial attacks, but also as a guide to diagnose/explain the limitation of current computer vision models as well as potential improving strategies. We hope this workshop can shed light on bridging the gap between the human vision system and computer vision systems, and chart out cross-community collaborations, including computer vision, machine learning and security communities.



Awards

  • DeepMind Best Paper Award

    • Improving the affordability of robustness training for DNNs
    Sidharth Gupta (University of Illinois at Urbana-Champaign); Parijat Dube (IBM Research); Ashish Verma (IBM Research)

  • DeepMind Best Extended Abstract

    • On Certifying Robustness against Backdoor Attacks via Randomized Smoothing
    Binghui Wang (Duke University); Xiaoyu Cao (Duke University); Jinyuan Jia (Duke University ); Neil Zhenqiang Gong (Duke University)

  • DeepMind Travel Award

    • Tianfu Wu (NC State University)

    Nataniel Ruiz (Boston University)

    Jiawei Chen (Boston University)

    Sravanti Addepalli (Indian Institute of Science)

    Quentin Bouniot (CEA LIST)

    Jiachen Sun (University of Michigan)

    Kartik Gupta (Australian National University)

    Ligong Han (Rutgers University)


    Schedule

    08:30 - 08:40         Opening Remark

    08:40 - 09:10         Invited Talk 1: Alan Yuille - Defending Against Random Occluder Attacks

    09:10 - 09:40         Invited Talk 2: Aleksander Madry - What Do Our Models Learn?

    09:40 - 10:10         Invited Talk 3: Earlence Fernandes - Physical Attacks on Object Detectors

    10:10 - 10:40         Invited Talk 4: Matthias Bethge - Testing Generalization

    10:40 - 11:10         Panel Discussion I: Alan Yuille, Aleksander Madry, Earlence Fernandes and Matthias Bethge

    11:10 - 13:00         Poster Session I

    13:00 - 14:00         Lunch Break

    14:00 - 14:30         Invited Talk 5: Laurens van der Maaten - Adversarial Robustness: The End of the Early Years

    14:30 - 15:00         Invited Talk 6: Pin-Yu Chen - Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness

    15:00 - 15:30         Invited Talk 7: Cho-Jui Hsieh - Adversarial Robustness of Discrete Machine Learning Models

    15:30 - 16:00         Invited Talk 8: Boqing Gong - Towards Visual Recognition in the Wild: Long-Tailed Sources and Open Compound Targets

    16:00 - 16:30         Invited Talk 9: Thomas G. Dietterich - Setting Alarm Thresholds for Anomaly Detection

    16:30 - 17:00         Panel Discussion II: Laurens van der Maaten, Pin-Yu Chen, Cho-Jui Hsieh, Boqing Gong and Thomas G. Dietterich

    17:00 - 18:50         Poster Session II

    18:50 - 19:00         Closing Remark


    Accepted Papers

    Two Live Q&A Sessions for ALL Papers: 11:10 - 13:00   &   17:00 - 18:50 (PDT)

    Please find the zoom link of each paper at our internal website.

    Noise is Inside Me! Generating Adversarial Perturbations with Noise Derived from Natural Filters
    Akshay Agarwal (IIIT Delhi); Mayank Vatsa (IIT Jodhpur); Richa Singh (IIIT-Delhi); Nalini Ratha (IBM)

    Learning Ordered Top-k Adversarial Attacks via Adversarial Distillation
    Tianfu Wu (NC State University); Zekun Zhang (NC state university)

    On Certifying Robustness against Backdoor Attacks via Randomized Smoothing
    Binghui Wang (Duke University); Xiaoyu Cao (Duke University); Jinyuan Jia (Duke University ); Neil Zhenqiang Gong (Duke University)

    Adversarial Fooling Beyond "Flipping the Label"
    Konda Reddy Mopuri (School of Informatics, University of Edinburgh); Vaisakh Shaj (University Of Lincoln); Venkatesh Babu Radhakrishnan (Indian Institute of Science)

    Improving the affordability of robustness training for DNNs
    Sidharth Gupta (University of Illinois at Urbana-Champaign); Parijat Dube (IBM Research); Ashish Verma (IBM Research)

    Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems
    Nataniel Ruiz (Boston University); Sarah Bargal (Boston University); Stan Sclaroff (Boston University)

    A Cyclically-Trained Adversarial Network for Invariant Representation Learning
    Jiawei Chen (Boston University ); Janusz Konrad (Boston University); Prakash Ishwar (Boston University)

    Role of Spatial Context in Adversarial Robustness for Object Detection
    Aniruddha Saha (UMBC); Akshayvarun Subramanya (UMBC); Koninika Patil (UMBC); Hamed Pirsiavash (UMBC)

    Extensions and limitations of randomized smoothing for robustness guarantees
    Jamie Hayes (University College London)

    Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers
    Loc Truong (Western Washington University); Chace Jones (Western Washington University); Nicole Nichols (PNNL); Andrew August (PNNL); Brian Hutchinson (Western Washington University); Brenda Praggastis (PNNL); Robert Jasper (PNNL); Aaron R Tuor (PNNL)

    Fooling Network Interpretation in Image Classification
    Akshayvarun Subramanya (UMBC); Vipin Pillai (UMBC); Hamed Pirsiavash (UMBC)

    Probing for Artifacts: Detecting Imagenet Model Evasions
    Jeremiah Rounds (PNNL); Addie Kingsland (PNNL; Michael Henry (PNNL); Kayla Duskin (PNNL)

    Robust Assessment of Real-World Adversarial Examples
    Carlos M Ortiz Marrero (PNNL); Brett Jefferson (PNNL)

    Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes
    Sravanti Addepalli (Indian Institute of Science); Vivek B S (Indian Institute of Science); Arya Baburaj (Indian Institute of Science); Gaurang Sriramanan (Indian Institute of Science); Venkatesh Babu Radhakrishnan (Indian Institute of Science)

    Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks
    Quentin Bouniot (CEA LIST); Angélique Loesch (CEA LIST); Romaric Audigier (CEA LIST)

    Data from Model: Extracting Data from Non-robust and Robust Models
    Philipp Benz (KAIST); Chaoning Zhang (KAIST); Tooba Imtiaz (KAIST); In So Kweon (KAIST, Korea)

    Universal Adversarial Perturbations are Not Bugs, They are Features
    Philipp Benz (KAIST); Chaoning Zhang (KAIST); Tooba Imtiaz (KAIST); In So Kweon (KAIST, Korea)

    Towards Robust LiDAR-based Perception in Autonomous Driving
    Jiachen Sun (University of Michigan); yulong cao (University of Michigan, Ann Arbor ); Qi Alfred Chen (UC Irvine); Zhuoqing Morley Mao (University of Michigan)

    Improved Gradient based Adversarial Attacks for Quantized Networks
    Kartik Gupta (Australian National University); Thalaiyasingam Ajanthan (ANU)

    Live Trojan Attacks on Deep Neural Networks
    Robby S Costales (Columbia University); Chengzhi Mao (Columbia University); Raphael Norwitz (Nutanix); Bryan Kim (Stanford University); Junfeng Yang (Columbia University)

    Multiview-Robust 3D Adversarial Examples of Real-world Objects
    Philip Yao (University of Michigan); Andrew So (California State Polytechnic University at Pomona); Tingting Chen (California State Polytechnic University at Pomona); Hao Ji (California State Polytechnic University at Pomona)

    Unbiased Auxiliary Classifier GANs with MINE
    Ligong Han (Rutgers University); Anastasis Stathopoulos (Rutgers University); Tao Xue (Rutgers University); Dimitris N. Metaxas (Rutgers)

    Crafting Adversarial Examples on 3D Object Detection Sensor Fusion Models
    Won Park (University of Michigan); Qi Alfred Chen (UC Irvine); Zhuoqing Morley Mao (University of Michigan)

    Transferable Adversarial Attacks on Deep Reinforcement Learning
    Xinlei Pan (UC Berkeley); Yulong Cao (University of Michigan); Xindi Wu (Carnegie Mellon University ); Eric Zelikman (Stanford University); Chaowei Xiao (University of Michigan; Yanan Sui; Rudrasis Chakraborty (UC Berkeley/ICSI); Ronald Fearing (UC Berkeley)

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    Speakers

    Alan Yuille
    Johns Hopkins University
    Aleksander Mądry
    MIT
    Earlence Fernandes
    UW–Madison
    Matthias Bethge
    University of Tübingen

    Laurens van der Maaten
    Facebook AI Research
    Pin-Yu Chen
    IBM
    Cho-Jui Hsieh
    UCLA
    Boqing Gong
    Google
    Thomas G. Dietterich
    Oregon State University
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    Organizing Committee

    Cihang Xie
    Johns Hopkins University
    Xinyun Chen
    UC Berkeley
    Song Bai
    University of Oxford
    Bo Li
    UIUC
    Kaiming He
    Facebook AI Research

    Fei-Fei Li
    Stanford University
    Luc Van Gool
    ETH Zurich
    Philip H.S. Torr
    University of Oxford
    Dawn Song
    UC Berkeley
    Alan Yuille
    Johns Hopkins University

    Program Committee

    • Maksym Andriushchenko (EPFL)
    • Anurag Arnab (Google)
    • Arjun Nitin Bhagoji (Princeton University)
    • Wieland Brendel (University of Tübingen)
    • Yulong Cao (University of Michigan)
    • Hongge Chen (MIT)
    • Ambra Demontis (University of Cagliari)
    • Yinpeng Dong (Tsinghua University)
    • Sven Gowal (DeepMind)
    • Chuan Guo (Cornell University)
    • Saumya Jetley (INRIA)
    • Adam Kortylewski (Johns Hopkins University)
    • Alexey Kurakin (Google Brain)
    • Yingwei Li (Johns Hopkins University)
    • Jingyue Lu (University of Oxford)
    • Jan Hendrik Metzen (Bosch Center for Artificial Intelligence)
    • Mahyar Najibi (University of Maryland, College Park)
    • Tianyu Pang (Tsinghua University)
    • Maura Pintor (University of Cagliari)
    • Hamed Pirsiavash (UMBC)
    • Omid Poursaeed (Cornell University)
    • Aaditya Prakash (PathAI)
    • Chongli Qin (DeepMind)
    • Jonas Rauber (University of Tübingen)
    • Aniruddha Saha (UMBC)
    • Ali Shafahi (University of Maryland, College Park)
    • Yash Sharma (University of Tübingen)
    • Akshayvarun Subramanya (UMBC)
    • Krishna Kumar Singh (UC Davis)
    • David Stutz (Max Planck Institute for Informatics)
    • Peng Tang (Salesforce Research)
    • Jianyu Wang (Waymo)
    • Yuxin Wu (Facebook AI Research)
    • Chang Xiao (Columbia University)
    • Chaowei Xiao (University of Michigan)
    • Hongyang Zhang (Toyota Technological Institute at Chicago)
    • Huan Zhang (UCLA)
    • Dan Xu (University of Oxford)

    Sponsor


    Please contact Cihang Xie or Xinyun Chen if you have questions. The webpage template is by the courtesy of ICCV 2019 Tutorial on Interpretable Machine Learning for Computer Vision. Thank Yingwei Li for making this website.