Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Hamadene, A.; Allili, M. S.
Cross-Model Deepfake Detection Through Contourlet-Based Inter-Channel Spectral Analysis Journal Article
In: IEEE Transactions on Biometrics, Behavior, and Identity Science, 2026, ISSN: 26376407 (ISSN).
Abstract | Links | BibTeX | Tags: Attribution, Computer forensics, Computer vision, Contourlet transform, Contourlets, Cross model, Cross-Model Generalization, Deep learning, Deepfake detection, Forensic engineering, Image Enhancement, Inter-channel contourlet feature, Inter-Channel Contourlet Features, Model generalization, Performance, Public trust, Spectral analyze, Spectrum analysis
@article{hamadeneCrossModelDeepfakeDetection2026,
title = {Cross-Model Deepfake Detection Through Contourlet-Based Inter-Channel Spectral Analysis},
author = {A. Hamadene and M. S. Allili},
url = {https://www.scopus.com/pages/publications/105038690534?origin=resultslist},
doi = {10.1109/TBIOM.2026.3687469},
issn = {26376407 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The rapid advancement of generative AI has fueled the proliferation of highly realistic deepfakes, raising growing concerns for public trust, digital forensics, and societal stability. While deep learning-based detectors have shown promising results in controlled settings, their effectiveness diminishes significantly across different datasets or unseen deepfakes. This performance drop reveals a core limitation: overfitting to dataset-specific artifacts and a failure to learn robust, content-agnostic forensic cues. In this work, we study a novel deepfake characterization that targets inter-channel spectral inconsistencies, a subtle but critical artifact of AI-generated imagery. Departing from conventional approaches that focus on spatial irregularities or within-channel frequency analysis, our method leverages the Contourlet Transform (CT) to extract high-frequency, multiscale, and multidirectional features across color channels. These features reveal latent spectral distortions and structural misalignments that persist across different generative models, including both GANs and Diffusion Models. Extensive experiments show that our approach generalizes well across datasets, achieving strong performance in both model-specific and cross-model detection tasks. Our findings highlight the promise of inter-channel spectral analysis for advancing the robustness and generalizability of deepfake detection systems. © 2019 IEEE.},
keywords = {Attribution, Computer forensics, Computer vision, Contourlet transform, Contourlets, Cross model, Cross-Model Generalization, Deep learning, Deepfake detection, Forensic engineering, Image Enhancement, Inter-channel contourlet feature, Inter-Channel Contourlet Features, Model generalization, Performance, Public trust, Spectral analyze, Spectrum analysis},
pubstate = {published},
tppubtype = {article}
}
The rapid advancement of generative AI has fueled the proliferation of highly realistic deepfakes, raising growing concerns for public trust, digital forensics, and societal stability. While deep learning-based detectors have shown promising results in controlled settings, their effectiveness diminishes significantly across different datasets or unseen deepfakes. This performance drop reveals a core limitation: overfitting to dataset-specific artifacts and a failure to learn robust, content-agnostic forensic cues. In this work, we study a novel deepfake characterization that targets inter-channel spectral inconsistencies, a subtle but critical artifact of AI-generated imagery. Departing from conventional approaches that focus on spatial irregularities or within-channel frequency analysis, our method leverages the Contourlet Transform (CT) to extract high-frequency, multiscale, and multidirectional features across color channels. These features reveal latent spectral distortions and structural misalignments that persist across different generative models, including both GANs and Diffusion Models. Extensive experiments show that our approach generalizes well across datasets, achieving strong performance in both model-specific and cross-model detection tasks. Our findings highlight the promise of inter-channel spectral analysis for advancing the robustness and generalizability of deepfake detection systems. © 2019 IEEE.
2.
Brideau-Duquette, M.; Côté, S. S. -P.; Boukhalfi, T.; Renaud, P.
Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity Proceedings Article
In: B., Boulay; T., Di Mascio; E., Tovar; C., Meinel (Ed.): International Conference on Computer Supported Education, CSEDU - Proceedings, pp. 856–864, Science and Technology Publications, Lda, 2025, ISBN: 21845026 (ISSN); 978-989758746-7 (ISBN), (Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services
@inproceedings{brideau-duquette_forensic_2025,
title = {Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity},
author = {M. Brideau-Duquette and S. S. -P. Côté and T. Boukhalfi and P. Renaud},
editor = {Boulay B. and Di Mascio T. and Tovar E. and Meinel C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003621528&doi=10.5220%2f0013496100003932&partnerID=40&md5=ee442c9113f1104973b40ef698bc5d7d},
doi = {10.5220/0013496100003932},
isbn = {21845026 (ISSN); 978-989758746-7 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},
volume = {1},
pages = {856–864},
publisher = {Science and Technology Publications, Lda},
abstract = {The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.},
note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},
keywords = {Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
3.
Brideau-Duquette, M.; Côté, S. S. -P.; Boukhalfi, T.; Renaud, P.
Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity Proceedings Article
In: B., Boulay; T., Di Mascio; E., Tovar; C., Meinel (Ed.): International Conference on Computer Supported Education, CSEDU - Proceedings, pp. 856–864, Science and Technology Publications, Lda, 2025, ISBN: 21845026 (ISSN); 978-989758746-7 (ISBN), (Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services
@inproceedings{brideau-duquetteForensicPsychiatryBig2025a,
title = {Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity},
author = {M. Brideau-Duquette and S. S. -P. Côté and T. Boukhalfi and P. Renaud},
editor = {Boulay B. and Di Mascio T. and Tovar E. and Meinel C.},
url = {https://www.scopus.com/pages/publications/105003621528?origin=resultslist},
doi = {10.5220/0013496100003932},
isbn = {21845026 (ISSN); 978-989758746-7 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},
volume = {1},
pages = {856–864},
publisher = {Science and Technology Publications, Lda},
abstract = {The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.},
note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},
keywords = {Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
4.
Brideau-Duquette, M.; Côté, S. S. -P.; Boukhalfi, T.; Renaud, P.
Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity Proceedings Article
In: B., Boulay; T., Di Mascio; E., Tovar; C., Meinel (Ed.): International Conference on Computer Supported Education, CSEDU - Proceedings, pp. 856–864, Science and Technology Publications, Lda, 2025, ISBN: 21845026 (ISSN); 978-989758746-7 (ISBN), (Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services
@inproceedings{brideau-duquetteForensicPsychiatryBig2025,
title = {Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity},
author = {M. Brideau-Duquette and S. S. -P. Côté and T. Boukhalfi and P. Renaud},
editor = {Boulay B. and Di Mascio T. and Tovar E. and Meinel C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003621528&doi=10.5220%2f0013496100003932&partnerID=40&md5=ee442c9113f1104973b40ef698bc5d7d},
doi = {10.5220/0013496100003932},
isbn = {21845026 (ISSN); 978-989758746-7 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},
volume = {1},
pages = {856–864},
publisher = {Science and Technology Publications, Lda},
abstract = {The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.},
note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},
keywords = {Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
5.
Boukhalfi, T.; Joyal, C.; Bouchard, S.; Neveu, S. M.; Renaud, P.
Tools and techniques for real-time data acquisition and analysis in brain computer interface studies using qEEG and eye tracking in virtual reality environment Proceedings Article
In: A., Zaremba M. Sasiadek J. Dolgui (Ed.): IFAC-PapersOnLine, pp. 46–51, 2015, ISSN: 24058963, (ISSN: 24058963 Issue: 3).
Abstract | Links | BibTeX | Tags: Acquisition, Analysis, brain computer interface, Data acquisition, Electroencephalography, eye tracking, Eye-tracking, Forensic, Forensic engineering, Heat maps, Interfaces (computer), Psychiatry, Real time, virtual reality
@inproceedings{boukhalfi_tools_2015,
title = {Tools and techniques for real-time data acquisition and analysis in brain computer interface studies using qEEG and eye tracking in virtual reality environment},
author = {T. Boukhalfi and C. Joyal and S. Bouchard and S. M. Neveu and P. Renaud},
editor = {Zaremba M. Sasiadek J. Dolgui A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953850845&doi=10.1016%2fj.ifacol.2015.06.056&partnerID=40&md5=5eaa3cab6df7946f02c74e4aded68eac},
doi = {10.1016/j.ifacol.2015.06.056},
issn = {24058963},
year = {2015},
date = {2015-01-01},
booktitle = {IFAC-PapersOnLine},
volume = {28},
number = {3},
pages = {46–51},
abstract = {In this paper, we present the pipeline of data acquisition and analysis used in the ARViPL lab at the Montreal Philippe-Pinel Institute for different studies related to forensic psychiatry in Virtual Reality (VR) environment and we discuss the different challenges that we encounter during the experiments when combining different new technologies that help researchers to better understand the underlying mechanisms of various mental health disorders. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
note = {ISSN: 24058963
Issue: 3},
keywords = {Acquisition, Analysis, brain computer interface, Data acquisition, Electroencephalography, eye tracking, Eye-tracking, Forensic, Forensic engineering, Heat maps, Interfaces (computer), Psychiatry, Real time, virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present the pipeline of data acquisition and analysis used in the ARViPL lab at the Montreal Philippe-Pinel Institute for different studies related to forensic psychiatry in Virtual Reality (VR) environment and we discuss the different challenges that we encounter during the experiments when combining different new technologies that help researchers to better understand the underlying mechanisms of various mental health disorders. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
6.
Boukhalfi, T.; Joyal, C.; Bouchard, S.; Neveu, S. M.; Renaud, P.
Tools and techniques for real-time data acquisition and analysis in brain computer interface studies using qEEG and eye tracking in virtual reality environment Proceedings Article
In: A., Zaremba M. Sasiadek J. Dolgui (Ed.): IFAC-PapersOnLine, pp. 46–51, 2015, ISSN: 24058963.
Abstract | Links | BibTeX | Tags: Acquisition, Analysis, brain computer interface, Data acquisition, Electroencephalography, eye tracking, Eye-tracking, Forensic, Forensic engineering, Heat maps, Interfaces (computer), Psychiatry, Real time, virtual reality
@inproceedings{boukhalfiToolsTechniquesRealtime2015,
title = {Tools and techniques for real-time data acquisition and analysis in brain computer interface studies using qEEG and eye tracking in virtual reality environment},
author = {T. Boukhalfi and C. Joyal and S. Bouchard and S. M. Neveu and P. Renaud},
editor = {Zaremba M. Sasiadek J. Dolgui A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953850845&doi=10.1016%2fj.ifacol.2015.06.056&partnerID=40&md5=5eaa3cab6df7946f02c74e4aded68eac},
doi = {10.1016/j.ifacol.2015.06.056},
issn = {24058963},
year = {2015},
date = {2015-01-01},
booktitle = {IFAC-PapersOnLine},
volume = {28},
number = {3},
pages = {46–51},
abstract = {In this paper, we present the pipeline of data acquisition and analysis used in the ARViPL lab at the Montreal Philippe-Pinel Institute for different studies related to forensic psychiatry in Virtual Reality (VR) environment and we discuss the different challenges that we encounter during the experiments when combining different new technologies that help researchers to better understand the underlying mechanisms of various mental health disorders. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
keywords = {Acquisition, Analysis, brain computer interface, Data acquisition, Electroencephalography, eye tracking, Eye-tracking, Forensic, Forensic engineering, Heat maps, Interfaces (computer), Psychiatry, Real time, virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present the pipeline of data acquisition and analysis used in the ARViPL lab at the Montreal Philippe-Pinel Institute for different studies related to forensic psychiatry in Virtual Reality (VR) environment and we discuss the different challenges that we encounter during the experiments when combining different new technologies that help researchers to better understand the underlying mechanisms of various mental health disorders. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.