Workshop Program

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All time in the program schedule are US EDT (UTC−04:00)

9:00-9:20 Opening remarks

Chengmo Yang, University of Delaware

9:20-10:50 Session One: Data and model implementation vulnerabilities

Session Chair: Yingjie Lao, Clemson University

9:20-10:20 Talk 1 ContraNet: Detecting Adversarial Examples Based on Semantic Contradiction

Qiang Xu, The Chinese University of Hong Kong

9:50-10:20 Talk 2 Physical Adversarial Example and Defense with Compactness

Xue Lin, Northeastern University

10:20-10:50 Talk 3 Security Vulnerabilities of Deep Neural Network Execution

Yunsi Fei, Northeastern University

10:50-11:00 Break

11:00-12:30 Session Two: Fault tolerance and fault injection attacks

Session Chair: Caiwen Ding, University of Connecticut

11:00-11:30 Talk 4 FTT-NAS: Discovering Fault-tolerant Convolutional Neural Architecture

Yu Wang, Tsinghua University

11:30-12:00 Talk 5 Exploring Adversarial Weight Attack and Defense through Hardware Fault Injection in Deep Learning Computing System

Deliang Fan, Arizona State University

12:00-12:30 Talk 6 Comprehensive but Free: Injecting DNNs with Algorithmic Antibodies to Resist Faults

Alex Orailoglu, University of California San Diego

12:30-13:00 Lunch break

13:30-15:30 Session Three: Emerging hardware threats and countermeasures

Session Chair: Chen Liu, Intel

13:30-14:00 Talk 7 Scan-Chain Attacks: A New Hardware Threat for Stealing Machine Learning Models

Aydin Aysu, North Carolina State University

14:00-14:30 Talk 8 Hardware Backdoor and Watermarking for Deep Learning Systems

Yingjie Lao, Clemson University

14:30-15:00 Talk 9 MIDAS: Hardware based Mitigation against Model Inversion Attacks

Gang Qu, University of Maryland College Park

15:00-15:30 Talk 10 Homomorphic Encryption

Ro Cammarota, Intel

15:30-15:45 Closing remarks

Wujie Wen, Lehigh University

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