This page presents our paper:
Title: Discrete Optimal Transport is a Strong Audio Adversarial Attack
Authors: Anton Selitskiy, Akib Shahriyar, and Jishnuraj Prakasan
PDF: Download paper
Code: on GitHub
In this paper, we introduce the discrete optimal transport voice conversion (kDOT-VC) method. Comparison with kNN-VC, SinkVC, and Gaussian optimal transport (factorized MKL) demon- strates stronger domain adaptation abilities of our method. We use the probabilistic nature of optimal transport (OT) and show that kDOT-VC is an effective black-box adversarial attack against modern audio anti-spoofing countermeasures (CMs). Our attack operates as a post-processing, distribution-alignment step: frame-level WavLM embeddings of generated speech are aligned to an unpaired bona fide pool via entropic OT and a top- k barycentric projection, then decoded with a neural vocoder. Ablation analysis indicates that distribution-level alignment is a powerful and stable attack for deployed CMs.
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