2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-5.3
Paper Title ADVERSARIAL ATTACKS ON COARSE-TO-FINE CLASSIFIERS
Authors Ismail Alkhouri, George Atia, University of Central Florida, United States
SessionMLSP-5: Machine Learning for Classification Applications 2
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-PRCL] Pattern recognition and classification
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Adversarial attacks have exposed the vulnerability of one-stage classifiers to carefully crafted perturbations which were shown to drastically alter their predictions while remaining imperceptible. In this paper, we examine the susceptibility of coarse-to-fine hierarchical classifiers to such types of attacks. We formulate convex programs to generate perturbations attacking these models and propose a generic solution based on the Alternating Direction Method of Multipliers (ADMM). We evaluate the performance of the proposed models using the degradation in classification accuracy and imperceptibility measures in comparison to perturbations generated to fool one-stage classifiers.