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Forensics for Adversarial Machine Learning through Attack Mapping Identification

Published in IEEE International Conference on Acoustics, Speech and Signal Processing, 2023, 2023

This paper proposes an attack mapping identification method that utilizes a pre-attack example recovery mechanism as a feature extraction method for performing post-attack forensic analysis of test-time adversarial attacks.

Recommended citation: A. Yan, J. Kim and R. Raich. (2023). "Forensics for Adversarial Machine Learning Through Attack Mapping Identification." ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10095092

Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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