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 Proceedings Article
In: K., Adi; O., Nguena Timo; N., Boulahia-Cuppens; D., Espes; N., Stakhanova; M., Omar (Ed.): Lect. Notes Comput. Sci., pp. 376–389, Springer Science and Business Media Deutschland GmbH, 2026, ISBN: 03029743 (ISSN); 978-303220731-9 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Abstract | Links | BibTeX | Tags: 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.
Allili, M. S.; Ziou, D.
Automatic change detection and updating of topographic databases by using satellite imagery: A level set approach Journal Article
In: Geomatica, vol. 59, no. 3, pp. 275–281, 2005, ISSN: 11951036.
Abstract | Links | BibTeX | Tags: Automatic change detection, Database systems, detection method, Edge detection, Feature extraction, Geographic information systems, Image analysis, Image segmentation, Imaging techniques, Landsat thematic mapper, Mathematical models, Probability, Remote sensing, Satellite imagery, Segmentation, spatial data, Topographic databases, topographic mapping
@article{allili_automatic_2005-2,
title = {Automatic change detection and updating of topographic databases by using satellite imagery: A level set approach},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-28444481044&partnerID=40&md5=e5d147401a402b14084292a2eeb6a792},
issn = {11951036},
year = {2005},
date = {2005-01-01},
journal = {Geomatica},
volume = {59},
number = {3},
pages = {275–281},
abstract = {In order to keep up-to-date geospatial data in topographic databases, automatic change detection and data updating is required. In the present paper, we investigate the automatic change detection of geospatial data by using level set active contours. We propose an approach that is based on region comparison between two multi-temporal datasets. Firstly, the regions are extracted from two co-registered images taken apart in time by using level set based active contours segmentation. Then, the change detection is performed by spatially comparing the resulting region segments from the two images. The approach is validated by experiments relating to the change detection of lake surfaces by using Landsat7 multi-spectral imagery.},
keywords = {Automatic change detection, Database systems, detection method, Edge detection, Feature extraction, Geographic information systems, Image analysis, Image segmentation, Imaging techniques, Landsat thematic mapper, Mathematical models, Probability, Remote sensing, Satellite imagery, Segmentation, spatial data, Topographic databases, topographic mapping},
pubstate = {published},
tppubtype = {article}
}
In order to keep up-to-date geospatial data in topographic databases, automatic change detection and data updating is required. In the present paper, we investigate the automatic change detection of geospatial data by using level set active contours. We propose an approach that is based on region comparison between two multi-temporal datasets. Firstly, the regions are extracted from two co-registered images taken apart in time by using level set based active contours segmentation. Then, the change detection is performed by spatially comparing the resulting region segments from the two images. The approach is validated by experiments relating to the change detection of lake surfaces by using Landsat7 multi-spectral imagery.
3.
Allili, M. S.; Ziou, D.
Automatic change detection and updating of topographic databases by using satellite imagery: A level set approach Journal Article
In: Geomatica, vol. 59, no. 3, pp. 275–281, 2005, ISSN: 11951036 (ISSN).
Abstract | Links | BibTeX | Tags: Automatic change detection, Database systems, detection method, Edge detection, Feature extraction, Geographic information systems, Image analysis, Image segmentation, Imaging techniques, Landsat thematic mapper, Mathematical models, Probability, Remote sensing, Satellite imagery, Segmentation, spatial data, Topographic databases, topographic mapping
@article{alliliAutomaticChangeDetection2005,
title = {Automatic change detection and updating of topographic databases by using satellite imagery: A level set approach},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-28444481044&partnerID=40&md5=e5d147401a402b14084292a2eeb6a792},
issn = {11951036 (ISSN)},
year = {2005},
date = {2005-01-01},
journal = {Geomatica},
volume = {59},
number = {3},
pages = {275–281},
abstract = {In order to keep up-to-date geospatial data in topographic databases, automatic change detection and data updating is required. In the present paper, we investigate the automatic change detection of geospatial data by using level set active contours. We propose an approach that is based on region comparison between two multi-temporal datasets. Firstly, the regions are extracted from two co-registered images taken apart in time by using level set based active contours segmentation. Then, the change detection is performed by spatially comparing the resulting region segments from the two images. The approach is validated by experiments relating to the change detection of lake surfaces by using Landsat7 multi-spectral imagery.},
keywords = {Automatic change detection, Database systems, detection method, Edge detection, Feature extraction, Geographic information systems, Image analysis, Image segmentation, Imaging techniques, Landsat thematic mapper, Mathematical models, Probability, Remote sensing, Satellite imagery, Segmentation, spatial data, Topographic databases, topographic mapping},
pubstate = {published},
tppubtype = {article}
}
In order to keep up-to-date geospatial data in topographic databases, automatic change detection and data updating is required. In the present paper, we investigate the automatic change detection of geospatial data by using level set active contours. We propose an approach that is based on region comparison between two multi-temporal datasets. Firstly, the regions are extracted from two co-registered images taken apart in time by using level set based active contours segmentation. Then, the change detection is performed by spatially comparing the resulting region segments from the two images. The approach is validated by experiments relating to the change detection of lake surfaces by using Landsat7 multi-spectral imagery.