@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}

}

@inproceedings{tiwariQuantifyingBiologicalSex2026,

title = {Quantifying Biological Sex Leakage in Electroencephalography-Based Mental Workload Measurement and Its Impact on Model Performance},

author = {A. Tiwari and R. Arrabito and A. Davoust and T. H. Falk},

url = {https://www.scopus.com/pages/publications/105045575255?origin=resultslist},

doi = {10.1109/ICHMS69701.2026.11602273},

isbn = {979-833154511-6 (ISBN)},

year  = {2026},

date = {2026-01-01},

booktitle = {IEEE Int. Conf. Hum.-Mach. Syst., ICHMS},

pages = {392–397},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Detection of mental workload is essential for designing systems that can monitor and adapt to the needs of human operators, thereby ensuring their optimal performance and well-being. Features derived from neurophysiological signals, particularly electroencephalograms (EEG), provide a realtime, unobtrusive, and objective indicator of mental workload. However, EEG signals are often correlated with demographic variables, including biological sex and age, which can introduce systematic performance differences across demographic groups, while unintentionally encoding demographic information into models trained on EEG data. In this work, we investigate and quantify the influence of user demographics on mental workload detection models using fairness analysis. Furthermore, we propose domain-adversarial training, in which an adversary penalizes the model for retaining sex-related information, to mitigate leakage of private demographic information (biological sex). The proposed approach simultaneously increases the odds ratio for sex from 0.83 to 0.90 (values closer to 1 indicate reduced demographic disparity) while reducing statistical significance. This approach, however, also leads to a reduction in mental workload detection performance, with a 12.7% decrease in balanced accuracy (BACC), suggesting an inherent trade-off between performance and privacy protection. © 2026 IEEE.},

note = {Journal Abbreviation: IEEE Int. Conf. Hum.-Mach. Syst., ICHMS},

keywords = {Biomedical signal processing, Demographic information, Designing systems, Economic and social effects, Electroencephalography, Electrophysiology, Human operator, Mental workload, Modeling performance, Optimal performance, Performance, Population statistics, Real- time, Well being, Workload measurements},

pubstate = {published},

tppubtype = {inproceedings}

}