Result Details
Joint Energy-Based Model for Robust Speech Classification System against Dirty-Label Backdoor Poisoning Attacks
Our novel technique utilizes a Joint Energy-based Model (JEM) that integrates both discriminative and generative approaches to increase resistance against dirty-label backdoor attacks. Our approach is especially effective when the trigger is short or hardly perceivable. We simulate the attack on the Speech Commands Dataset consisting of 1 s audio clips. During training, we use JEM to model a view of the input implemented by a randomly selected 610 ms window. During inference, we combine all (40) possible views utilizing a generative part of JEM. The resulting system has slightly decreased accuracy but significantly increased resistance shown in multiple scenarios. Interestingly, replacing JEM with a standard discriminative model (Disc) provides increased resistance with a lesser effect compared to JEM but maintains accuracy. We introduce an extension motivated by semi-supervised training that further improves JEM but not Disc. JEM can also benefit from Gaussian noise during evaluation.
joint energy-based model, poisoning attacks, speech commands classification, dirty-label backdoor
@inproceedings{BUT187975,
author="ŠŮSTEK, M. and JOSHI, S. and LI, H. and THEBAUD, T. and VILLALBA LOPEZ, J. and KHUDANPUR, S. and DEHAK, N.",
title="Joint Energy-Based Model for Robust Speech Classification System against Dirty-Label Backdoor Poisoning Attacks",
booktitle="Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)",
year="2023",
pages="1--8",
publisher="IEEE Signal Processing Society",
address="Taipei",
doi="10.1109/ASRU57964.2023.10389697",
isbn="979-8-3503-0689-7",
url="https://ieeexplore.ieee.org/document/10389697"
}