30 citations · 49 across the 12 of their papers we have counts for
12 papers
APEX: Audio Prototype EXplanations for Classification Tasks
Piotr Kawa, Kornel Howil, Piotr Borycki +3
Explainable AI (XAI) has achieved remarkable success in image classification, yet the audio domain lacks equally mature solutions. Current methods apply vision-based attribution te…
Replay Attacks Against Audio Deepfake Detection
Nicolas Müller, Piotr Kawa, Wei-Herng Choong +5
We show how replay attacks undermine audio deepfake detection: By playing and re-recording deepfake audio through various speakers and microphones, we make spoofed samples appear a…
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…
DeePen: Penetration Testing for Audio Deepfake Detection
Nicolas Müller, Piotr Kawa, Adriana Stan +5
Deepfakes - manipulated or forged audio and video media - pose significant security risks to individuals, organizations, and society at large. To address these challenges, machine…
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…
A New Approach to Voice Authenticity
Nicolas M. Müller, Piotr Kawa, Shen Hu +4
Voice faking, driven primarily by recent advances in text-to-speech (TTS) synthesis technology, poses significant societal challenges. Currently, the prevailing assumption is that…
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…
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…
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…
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…
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…
A Note on Deepfake Detection with Low-Resources
Piotr Kawa, Piotr Syga
Deepfakes are videos that include changes, quite often substituting face of a portrayed individual with a different face using neural networks. Even though the technology gained it…