30 citations · 69 across the 11 of their papers we have counts for
11 papers
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…
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…
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…
Robust watermarking with double detector-discriminator approach
Marcin Plata, Piotr Syga
In this paper we present a novel deep framework for a watermarking - a technique of embedding a transparent message into an image in a way that allows retrieving the message from a…
Robust Spatial-spread Deep Neural Image Watermarking
Marcin Plata, Piotr Syga
Watermarking is an operation of embedding an information into an image in a way that allows to identify ownership of the image despite applying some distortions on it. In this pape…
Practical Fault-Tolerant Data Aggregation
Krzysztof Grining, Marek Klonowski, Piotr Syga
During Financial Cryptography 2012 Chan et al. presented a novel privacy-protection fault-tolerant data aggregation protocol. Comparing to previous work, their scheme guaranteed pr…