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20162025
most citedAttack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection

30 citations · 69 across the 11 of their papers we have counts for

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7 papers · 1 filter

cs.SD2025★ 2 cited

As Good as It KAN Get: High-Fidelity Audio Representation

Patryk Marszałek, Maciej Rut, Piotr Kawa +2

Implicit neural representations (INR) have gained prominence for efficiently encoding multimedia data, yet their applications in audio signals remain limited. This study introduces…

cs.SD2024★ 1 cited

Are audio DeepFake detection models polyglots?

Bartłomiej Marek, Piotr Kawa, Piotr Syga

Since the majority of audio DeepFake (DF) detection methods are trained on English-centric datasets, their applicability to non-English languages remains largely unexplored. In thi…

cs.SD2024★ 5 cited

MLAAD: The Multi-Language Audio Anti-Spoofing Dataset

Nicolas M. Müller, Piotr Kawa, Wei Herng Choong +6

This paper presents the Multi-Language Audio Anti-Spoofing Dataset (MLAAD), version 11: a dataset of synthetic audio to train and evaluate audio deepfake detection models. It featu…

cs.SD2023★ 2 cited

Improved DeepFake Detection Using Whisper Features

Piotr Kawa, Marcin Plata, Michał Czuba +2

With a recent influx of voice generation methods, the threat introduced by audio DeepFake (DF) is ever-increasing. Several different detection methods have been presented as a coun…

cs.SD2022★ 1 cited

Defense Against Adversarial Attacks on Audio DeepFake Detection

Piotr Kawa, Marcin Plata, Piotr Syga

Audio DeepFakes (DF) are artificially generated utterances created using deep learning, with the primary aim of fooling the listeners in a highly convincing manner. Their quality i…

cs.SD2022

SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection

Piotr Kawa, Marcin Plata, Piotr Syga

Audio DeepFakes are utterances generated with the use of deep neural networks. They are highly misleading and pose a threat due to use in fake news, impersonation, or extortion. In…

cs.SD2022★ 30 cited

Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection

Piotr Kawa, Marcin Plata, Piotr Syga

Audio DeepFakes allow the creation of high-quality, convincing utterances and therefore pose a threat due to its potential applications such as impersonation or fake news. Methods…