Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Moradi, A.; Zhu, Y.; Falk, T. H.
Towards Lightweight On-Device Audio Deepfake Detection Using Squeezeformers Article d'actes
Dans: K., Adi; O., Nguena Timo; N., Boulahia-Cuppens; D., Espes; N., Stakhanova; M., Omar (Ed.): Lect. Notes Comput. Sci., p. 376–389, Springer Science and Business Media Deutschland GmbH, 2026, ISBN: 03029743 (ISSN); 978-303220731-9 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Résumé | Liens | BibTeX | Étiquettes: Audio DeepFake Detection, Detection mechanism, Edge Computing, Edge detection, Foundation models, High-accuracy, Large scale systems, Large-scale systems, Lightweight, Memory footprint, Performance, Real- time
@inproceedings{moradiLightweightOnDeviceAudio2026,
title = {Towards Lightweight On-Device Audio Deepfake Detection Using Squeezeformers},
author = {A. Moradi and Y. Zhu and T. H. Falk},
editor = {Adi K. and Nguena Timo O. and Boulahia-Cuppens N. and Espes D. and Stakhanova N. and Omar M.},
url = {https://www.scopus.com/pages/publications/105046137533?origin=resultslist},
doi = {10.1007/978-3-032-20732-6_24},
isbn = {03029743 (ISSN); 978-303220731-9 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {16295 LNCS},
pages = {376–389},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The increasing threat of audio deepfakes necessitates detection mechanisms that can operate in real-time on resource-constrained edge devices. While large-scale systems, such as detectors based on speech foundation models, have demonstrated high accuracy, their computational and memory footprints make them ill-suited for on-device applications. This paper addresses this critical gap by investigating the key factors that influence the performance of lightweight deepfake detection models. We conduct a systematic comparison of model architectures, input feature choices, and data augmentation techniques, evaluating both deepfake detection accuracy and computational complexity across three datasets. Our findings show that with proper modeling choices, a lightweight model can achieve performance comparable to that of a much larger model while being approximately 100× smaller in size. This work provides actionable insights for developing efficient and effective audio deepfake detectors tailored for the constraints of edge computing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Audio DeepFake Detection, Detection mechanism, Edge Computing, Edge detection, Foundation models, High-accuracy, Large scale systems, Large-scale systems, Lightweight, Memory footprint, Performance, Real- time},
pubstate = {published},
tppubtype = {inproceedings}
}
The increasing threat of audio deepfakes necessitates detection mechanisms that can operate in real-time on resource-constrained edge devices. While large-scale systems, such as detectors based on speech foundation models, have demonstrated high accuracy, their computational and memory footprints make them ill-suited for on-device applications. This paper addresses this critical gap by investigating the key factors that influence the performance of lightweight deepfake detection models. We conduct a systematic comparison of model architectures, input feature choices, and data augmentation techniques, evaluating both deepfake detection accuracy and computational complexity across three datasets. Our findings show that with proper modeling choices, a lightweight model can achieve performance comparable to that of a much larger model while being approximately 100× smaller in size. This work provides actionable insights for developing efficient and effective audio deepfake detectors tailored for the constraints of edge computing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
2.
Sadfi, Y.; Amamou, H.; Davoust, A.; Avila, A. R.
Comparative Analysis of Machine Learning, LLMs, and RAG for Fake News Detection Article d'actes
Dans: IEEE Conf. Artif. Intell., CAI, p. 2128–2133, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833156039-3 (ISBN), (Journal Abbreviation: IEEE Conf. Artif. Intell., CAI).
Résumé | Liens | BibTeX | Étiquettes: Classification (of information), Comparative analyzes, Comprehensive assessment, decision making, Decision-making process, Fake detection, Generalisation, High-accuracy, Language model, Learning algorithms, Learning systems, Machine learning, Machine learning algorithms, Machine-learning, Network platforms, Performance
@inproceedings{sadfiComparativeAnalysisMachine2026,
title = {Comparative Analysis of Machine Learning, LLMs, and RAG for Fake News Detection},
author = {Y. Sadfi and H. Amamou and A. Davoust and A. R. Avila},
url = {https://www.scopus.com/pages/publications/105042025800?origin=resultslist},
doi = {10.1109/CAI68641.2026.11536343},
isbn = {979-833156039-3 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Conf. Artif. Intell., CAI},
pages = {2128–2133},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The rapid spread of misinformation on social network platforms poses a serious threat to the decision-making processes in democratic societies. To mitigate the problem and cope with new misinformation scenarios several datasets have been proposed, followed by the emergence of new methods for detecting fake news. While several comparative analyses have been proposed to benchmark these solutions, the ever-growing development of new methods requires up-to-date analysis. For instance, recent approaches based on retrieval-augmented generation (RAG) have yet to be assessed and compared to previous methods used for fake news detection. In this study, we aim to fill this gap by presenting a comprehensive assessment of classical machine learning (ML) algorithms, Transformer-based models, such as BERT, large language models (LLMs), and RAG-based classifiers. We adopt four traditional fake news datasets, including a Portuguese one. Results indicate that classical ML and BERT deliver strong performance on in-domain tasks, demonstrating efficiency and high accuracy. RAG-augmented LLMs, on the other hand, offer enhanced generalization and robustness in cross-domain and cross-dataset settings, but are outperformed by traditional approaches for in-domain evaluation. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Conf. Artif. Intell., CAI},
keywords = {Classification (of information), Comparative analyzes, Comprehensive assessment, decision making, Decision-making process, Fake detection, Generalisation, High-accuracy, Language model, Learning algorithms, Learning systems, Machine learning, Machine learning algorithms, Machine-learning, Network platforms, Performance},
pubstate = {published},
tppubtype = {inproceedings}
}
The rapid spread of misinformation on social network platforms poses a serious threat to the decision-making processes in democratic societies. To mitigate the problem and cope with new misinformation scenarios several datasets have been proposed, followed by the emergence of new methods for detecting fake news. While several comparative analyses have been proposed to benchmark these solutions, the ever-growing development of new methods requires up-to-date analysis. For instance, recent approaches based on retrieval-augmented generation (RAG) have yet to be assessed and compared to previous methods used for fake news detection. In this study, we aim to fill this gap by presenting a comprehensive assessment of classical machine learning (ML) algorithms, Transformer-based models, such as BERT, large language models (LLMs), and RAG-based classifiers. We adopt four traditional fake news datasets, including a Portuguese one. Results indicate that classical ML and BERT deliver strong performance on in-domain tasks, demonstrating efficiency and high accuracy. RAG-augmented LLMs, on the other hand, offer enhanced generalization and robustness in cross-domain and cross-dataset settings, but are outperformed by traditional approaches for in-domain evaluation. © 2026 IEEE.