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 Article de journal
Dans: IEEE Transactions on Biometrics, Behavior, and Identity Science, 2026, ISSN: 26376407 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: 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.
Yapi, D.; Mejri, M.; Allili, M. S.; Baaziz, N.
A learning-based approach for automatic defect detection in textile images Article d'actes
Dans: A., Zaremba M. Sasiadek J. Dolgui (Ed.): IFAC-PapersOnLine, p. 2423–2428, 2015, ISBN: 24058963 (ISSN), (Journal Abbreviation: IFAC-PapersOnLine).
Résumé | Liens | BibTeX | Étiquettes: Algorithms, Artificial intelligence, Automatic defect detections, Barium compounds, Bayes Classifier, Computational efficiency, Contourlets, Defect detection, Defect detection algorithm, Defects, Detection problems, Feature extraction, Feature extraction and classification, Gaussians, Image classification, Learning algorithms, Learning systems, Learning-based approach, Machine learning approaches, Mixture of generalized gaussians, Mixtures of generalized Gaussians (MoGG), Textile defect detection, Textile images, Textiles, Textures
@inproceedings{yapi_learning-based_2015,
title = {A learning-based approach for automatic defect detection in textile images},
author = {D. Yapi and M. Mejri and M. S. Allili and N. Baaziz},
editor = {Zaremba M. Sasiadek J. Dolgui A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953865559&doi=10.1016%2fj.ifacol.2015.06.451&partnerID=40&md5=3dd0ef4c27cbd55700f6511af5f46772},
doi = {10.1016/j.ifacol.2015.06.451},
isbn = {24058963 (ISSN)},
year = {2015},
date = {2015-01-01},
booktitle = {IFAC-PapersOnLine},
volume = {28},
number = {3},
pages = {2423–2428},
abstract = {This paper addresses the textile defect detection problem using a machine-learning approach. We propose a novel algorithm that uses supervised learning to classify textile textures in defect and non-defect classes based on suitable feature extraction and classification. We use statistical modeling of multi-scale contourlet image decomposition to obtain compact and accurate signatures for texture description. Our defect detection algorithm is based on two phases. In the first phase, using a training set of images, we extract reference defect-free signatures for each textile category. Then, we use the Bayes classifier (BC) to learn signatures of defected and non-defected classes. In the second phase, defects are detected on new images using the trained BC and an appropriate decomposition of images into blocks. Our algorithm has the capability to achieve highly accurate defect detection and localisation in textile textures while ensuring an efficient computational time. Compared to recent state-of-the-art methods, our algorithm has yielded better results on the standard TILDA database. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
note = {Journal Abbreviation: IFAC-PapersOnLine},
keywords = {Algorithms, Artificial intelligence, Automatic defect detections, Barium compounds, Bayes Classifier, Computational efficiency, Contourlets, Defect detection, Defect detection algorithm, Defects, Detection problems, Feature extraction, Feature extraction and classification, Gaussians, Image classification, Learning algorithms, Learning systems, Learning-based approach, Machine learning approaches, Mixture of generalized gaussians, Mixtures of generalized Gaussians (MoGG), Textile defect detection, Textile images, Textiles, Textures},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper addresses the textile defect detection problem using a machine-learning approach. We propose a novel algorithm that uses supervised learning to classify textile textures in defect and non-defect classes based on suitable feature extraction and classification. We use statistical modeling of multi-scale contourlet image decomposition to obtain compact and accurate signatures for texture description. Our defect detection algorithm is based on two phases. In the first phase, using a training set of images, we extract reference defect-free signatures for each textile category. Then, we use the Bayes classifier (BC) to learn signatures of defected and non-defected classes. In the second phase, defects are detected on new images using the trained BC and an appropriate decomposition of images into blocks. Our algorithm has the capability to achieve highly accurate defect detection and localisation in textile textures while ensuring an efficient computational time. Compared to recent state-of-the-art methods, our algorithm has yielded better results on the standard TILDA database. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
3.
Yapi, D.; Mejri, M.; Allili, M. S.; Baaziz, N.
A learning-based approach for automatic defect detection in textile images Article d'actes
Dans: A., Zaremba M. Sasiadek J. Dolgui (Ed.): IFAC-PapersOnLine, p. 2423–2428, 2015, ISBN: 24058963 (ISSN), (Journal Abbreviation: IFAC-PapersOnLine).
Résumé | Liens | BibTeX | Étiquettes: Algorithms, Artificial intelligence, Automatic defect detections, Barium compounds, Bayes Classifier, Computational efficiency, Contourlets, Defect detection, Defect detection algorithm, Defects, Detection problems, Feature extraction, Feature extraction and classification, Gaussians, Image classification, Learning algorithms, Learning systems, Learning-based approach, Machine learning approaches, Mixture of generalized gaussians, Mixtures of generalized Gaussians (MoGG), Textile defect detection, Textile images, Textiles, Textures
@inproceedings{yapiLearningbasedApproachAutomatic2015,
title = {A learning-based approach for automatic defect detection in textile images},
author = {D. Yapi and M. Mejri and M. S. Allili and N. Baaziz},
editor = {Zaremba M. Sasiadek J. Dolgui A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953865559&doi=10.1016%2fj.ifacol.2015.06.451&partnerID=40&md5=3dd0ef4c27cbd55700f6511af5f46772},
doi = {10.1016/j.ifacol.2015.06.451},
isbn = {24058963 (ISSN)},
year = {2015},
date = {2015-01-01},
booktitle = {IFAC-PapersOnLine},
volume = {28},
number = {3},
pages = {2423–2428},
abstract = {This paper addresses the textile defect detection problem using a machine-learning approach. We propose a novel algorithm that uses supervised learning to classify textile textures in defect and non-defect classes based on suitable feature extraction and classification. We use statistical modeling of multi-scale contourlet image decomposition to obtain compact and accurate signatures for texture description. Our defect detection algorithm is based on two phases. In the first phase, using a training set of images, we extract reference defect-free signatures for each textile category. Then, we use the Bayes classifier (BC) to learn signatures of defected and non-defected classes. In the second phase, defects are detected on new images using the trained BC and an appropriate decomposition of images into blocks. Our algorithm has the capability to achieve highly accurate defect detection and localisation in textile textures while ensuring an efficient computational time. Compared to recent state-of-the-art methods, our algorithm has yielded better results on the standard TILDA database. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
note = {Journal Abbreviation: IFAC-PapersOnLine},
keywords = {Algorithms, Artificial intelligence, Automatic defect detections, Barium compounds, Bayes Classifier, Computational efficiency, Contourlets, Defect detection, Defect detection algorithm, Defects, Detection problems, Feature extraction, Feature extraction and classification, Gaussians, Image classification, Learning algorithms, Learning systems, Learning-based approach, Machine learning approaches, Mixture of generalized gaussians, Mixtures of generalized Gaussians (MoGG), Textile defect detection, Textile images, Textiles, Textures},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper addresses the textile defect detection problem using a machine-learning approach. We propose a novel algorithm that uses supervised learning to classify textile textures in defect and non-defect classes based on suitable feature extraction and classification. We use statistical modeling of multi-scale contourlet image decomposition to obtain compact and accurate signatures for texture description. Our defect detection algorithm is based on two phases. In the first phase, using a training set of images, we extract reference defect-free signatures for each textile category. Then, we use the Bayes classifier (BC) to learn signatures of defected and non-defected classes. In the second phase, defects are detected on new images using the trained BC and an appropriate decomposition of images into blocks. Our algorithm has the capability to achieve highly accurate defect detection and localisation in textile textures while ensuring an efficient computational time. Compared to recent state-of-the-art methods, our algorithm has yielded better results on the standard TILDA database. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
4.
Allili, M. S.; Baaziz, N.
Contourlet-based texture retrieval using a mixture of generalized Gaussian distributions Article de journal
Dans: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6855 LNCS, no PART 2, p. 446–454, 2011, ISSN: 03029743, (ISBN: 9783642236778 Place: Seville).
Résumé | Liens | BibTeX | Étiquettes: Contourlet transform, Contourlets, Distribution modelling, Finite mixtures, Gaussian distribution, Generalized Gaussian Distributions, Image analysis, Kullback-Leibler divergence, Mixtures, Monte-Carlo sampling, Probability density function, Similarity measure, Statistical representations, Texture discrimination, Texture retrieval, Textures
@article{allili_contourlet-based_2011,
title = {Contourlet-based texture retrieval using a mixture of generalized Gaussian distributions},
author = {M. S. Allili and N. Baaziz},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052796353&doi=10.1007%2f978-3-642-23678-5_53&partnerID=40&md5=fde8aaeea1609c81747b0ab27a8c78ce},
doi = {10.1007/978-3-642-23678-5_53},
issn = {03029743},
year = {2011},
date = {2011-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {6855 LNCS},
number = {PART 2},
pages = {446–454},
abstract = {We address the texture retrieval problem using contourlet-based statistical representation. We propose a new contourlet distribution modelling using finite mixtures of generalized Gaussian distributions (MoGG). The MoGG allows to capture a wide range of contourlet histogram shapes, which provides better description and discrimination of texture than using single probability density functions (pdfs). We propose a model similarity measure based on Kullback-Leibler divergence (KLD) approximation using Monte-Carlo sampling methods. We show that our approach using a redundant contourlet transform yields better texture discrimination and retrieval results than using other methods of statistical-based wavelet/contourlet modelling. © 2011 Springer-Verlag.},
note = {ISBN: 9783642236778
Place: Seville},
keywords = {Contourlet transform, Contourlets, Distribution modelling, Finite mixtures, Gaussian distribution, Generalized Gaussian Distributions, Image analysis, Kullback-Leibler divergence, Mixtures, Monte-Carlo sampling, Probability density function, Similarity measure, Statistical representations, Texture discrimination, Texture retrieval, Textures},
pubstate = {published},
tppubtype = {article}
}
We address the texture retrieval problem using contourlet-based statistical representation. We propose a new contourlet distribution modelling using finite mixtures of generalized Gaussian distributions (MoGG). The MoGG allows to capture a wide range of contourlet histogram shapes, which provides better description and discrimination of texture than using single probability density functions (pdfs). We propose a model similarity measure based on Kullback-Leibler divergence (KLD) approximation using Monte-Carlo sampling methods. We show that our approach using a redundant contourlet transform yields better texture discrimination and retrieval results than using other methods of statistical-based wavelet/contourlet modelling. © 2011 Springer-Verlag.
5.
Allili, M. S.; Baaziz, N.
Contourlet-based texture retrieval using a mixture of generalized Gaussian distributions Article de journal
Dans: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6855 LNCS, no PART 2, p. 446–454, 2011, ISSN: 03029743, (ISBN: 9783642236778 Place: Seville).
Résumé | Liens | BibTeX | Étiquettes: Contourlet transform, Contourlets, Distribution modelling, Finite mixtures, Gaussian distribution, Generalized Gaussian Distributions, Image analysis, Kullback-Leibler divergence, Mixtures, Monte-Carlo sampling, Probability density function, Similarity measure, Statistical representations, Texture discrimination, Texture retrieval, Textures
@article{alliliContourletbasedTextureRetrieval2011,
title = {Contourlet-based texture retrieval using a mixture of generalized Gaussian distributions},
author = {M. S. Allili and N. Baaziz},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052796353&doi=10.1007%2f978-3-642-23678-5_53&partnerID=40&md5=fde8aaeea1609c81747b0ab27a8c78ce},
doi = {10.1007/978-3-642-23678-5_53},
issn = {03029743},
year = {2011},
date = {2011-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {6855 LNCS},
number = {PART 2},
pages = {446–454},
abstract = {We address the texture retrieval problem using contourlet-based statistical representation. We propose a new contourlet distribution modelling using finite mixtures of generalized Gaussian distributions (MoGG). The MoGG allows to capture a wide range of contourlet histogram shapes, which provides better description and discrimination of texture than using single probability density functions (pdfs). We propose a model similarity measure based on Kullback-Leibler divergence (KLD) approximation using Monte-Carlo sampling methods. We show that our approach using a redundant contourlet transform yields better texture discrimination and retrieval results than using other methods of statistical-based wavelet/contourlet modelling. © 2011 Springer-Verlag.},
note = {ISBN: 9783642236778
Place: Seville},
keywords = {Contourlet transform, Contourlets, Distribution modelling, Finite mixtures, Gaussian distribution, Generalized Gaussian Distributions, Image analysis, Kullback-Leibler divergence, Mixtures, Monte-Carlo sampling, Probability density function, Similarity measure, Statistical representations, Texture discrimination, Texture retrieval, Textures},
pubstate = {published},
tppubtype = {article}
}
We address the texture retrieval problem using contourlet-based statistical representation. We propose a new contourlet distribution modelling using finite mixtures of generalized Gaussian distributions (MoGG). The MoGG allows to capture a wide range of contourlet histogram shapes, which provides better description and discrimination of texture than using single probability density functions (pdfs). We propose a model similarity measure based on Kullback-Leibler divergence (KLD) approximation using Monte-Carlo sampling methods. We show that our approach using a redundant contourlet transform yields better texture discrimination and retrieval results than using other methods of statistical-based wavelet/contourlet modelling. © 2011 Springer-Verlag.