Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Allaoui, M. L.; Allili, M. S.
LG-TargetSeg: Deep semantic fusion via language-conditioned selective scanning for targeted medical image segmentation Article de journal
Dans: Information Fusion, vol. 139, 2027, ISSN: 15662535 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Computerized tomography, decoding, Deep vision-language fusion, Encoder-decoder, Gaussians, Medical image processing, Medical image segmentation, Modeling languages, Natural language processing systems, Reinforced gaussian dice loss, Selective scanning, Semantic fusion, Semantic segmentation, Semantics, Shallow levels, Signal encoding, State space methods, State-space modeling, State-space models, Training time
@article{allaouiLGTargetSegDeepSemantic2027,
title = {LG-TargetSeg: Deep semantic fusion via language-conditioned selective scanning for targeted medical image segmentation},
author = {M. L. Allaoui and M. S. Allili},
url = {https://www.scopus.com/pages/publications/105051242411?origin=resultslist},
doi = {10.1016/j.inffus.2026.104797},
issn = {15662535 (ISSN)},
year = {2027},
date = {2027-01-01},
journal = {Information Fusion},
volume = {139},
publisher = {Elsevier B.V.},
abstract = {Clinical language defines the semantic intent of targeted medical image segmentation, yet most vision–language methods incorporate it only at a shallow level or through training-time conditioning, leaving spatial reasoning largely language-blind. We propose LG-TargetSeg (Language-Guided Targeted Segmentation), a cross-modal framework that embeds clinical language directly into the selective scan of a Mamba-based encoder-decoder. Natural-language prompts, encoded by a frozen MedSigLIP foundation model, shape selective scanning throughout the encoder–decoder and additionally adapt the bottleneck state transition, alongside gated modulation and cross-attention, enabling semantic guidance to continuously shape feature propagation toward the target anatomy. A Text-Driven Selective Scan 2D (TD-SS2D) module achieves prompt-aware multi-scale fusion with linear complexity in the number of spatial tokens. To improve robustness to boundary ambiguity and class imbalance, we introduce the Reinforced Gaussian Dice Loss (RGDL) with learnable weighting parameters. Evaluated under both task-specific and universal generalization settings, LG-TargetSeg achieves state-of-the-art performance. Moreover, its prompt-driven selectivity remains reliable under genuine multi-target competition: on abdominal CT images containing up to 16 annotated organs, changing only the text prompt on a frozen model produces organ masks with less than 1% pairwise overlap. Additional materials and code are available at: https://github.com/MOHAMEDLamine77/LG-TargetSeg . © 2026 Elsevier B.V.},
keywords = {Computerized tomography, decoding, Deep vision-language fusion, Encoder-decoder, Gaussians, Medical image processing, Medical image segmentation, Modeling languages, Natural language processing systems, Reinforced gaussian dice loss, Selective scanning, Semantic fusion, Semantic segmentation, Semantics, Shallow levels, Signal encoding, State space methods, State-space modeling, State-space models, Training time},
pubstate = {published},
tppubtype = {article}
}
Clinical language defines the semantic intent of targeted medical image segmentation, yet most vision–language methods incorporate it only at a shallow level or through training-time conditioning, leaving spatial reasoning largely language-blind. We propose LG-TargetSeg (Language-Guided Targeted Segmentation), a cross-modal framework that embeds clinical language directly into the selective scan of a Mamba-based encoder-decoder. Natural-language prompts, encoded by a frozen MedSigLIP foundation model, shape selective scanning throughout the encoder–decoder and additionally adapt the bottleneck state transition, alongside gated modulation and cross-attention, enabling semantic guidance to continuously shape feature propagation toward the target anatomy. A Text-Driven Selective Scan 2D (TD-SS2D) module achieves prompt-aware multi-scale fusion with linear complexity in the number of spatial tokens. To improve robustness to boundary ambiguity and class imbalance, we introduce the Reinforced Gaussian Dice Loss (RGDL) with learnable weighting parameters. Evaluated under both task-specific and universal generalization settings, LG-TargetSeg achieves state-of-the-art performance. Moreover, its prompt-driven selectivity remains reliable under genuine multi-target competition: on abdominal CT images containing up to 16 annotated organs, changing only the text prompt on a frozen model produces organ masks with less than 1% pairwise overlap. Additional materials and code are available at: https://github.com/MOHAMEDLamine77/LG-TargetSeg . © 2026 Elsevier B.V.
2.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments Article de journal
Dans: IEEE Open Journal of the Computer Society, vol. 7, p. 492–503, 2026, ISSN: 26441268 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)
@article{abdollahzadeh_vision-based_2026,
title = {A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105029942397&doi=10.1109%2FOJCS.2026.3663268&partnerID=40&md5=b11484e035458c84b1d3f6780b92c91c},
doi = {10.1109/OJCS.2026.3663268},
issn = {26441268 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Open Journal of the Computer Society},
volume = {7},
pages = {492–503},
abstract = {Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},
pubstate = {published},
tppubtype = {article}
}
Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.
3.
Gapski, M. C. B.; Kawai, V. A. S.; Leticio, G. R.; Valem, L. P.; Pedronette, D. C. G.; Allili, M. S.
Graph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation Article de journal
Dans: Journal of the Brazilian Computer Society, vol. 32, no 1, p. 1731–1754, 2026, ISSN: 01046500 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Classification accuracy, Convolution, Convolutional neural networks, extraction, Feature extraction, Feature extractor, Feature Fusion, Feature representation, Features fusions, Graph Neural Networks, Graph representation, Graph structures, Graph theory, Graphic methods, Image classification, Labeled data, Rank Aggregation, Semi-supervised, Semi-supervised image classification, Supervised image classifications, Textures
@article{gapskiGraphNeuralNetworks2026,
title = {Graph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation},
author = {M. C. B. Gapski and V. A. S. Kawai and G. R. Leticio and L. P. Valem and D. C. G. Pedronette and M. S. Allili},
url = {https://www.scopus.com/pages/publications/105045179876?origin=resultslist},
doi = {10.5753/jbcs.2026.5880},
issn = {01046500 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Journal of the Brazilian Computer Society},
volume = {32},
number = {1},
pages = {1731–1754},
publisher = {Brazilian Computing Society},
abstract = {Feature extraction involves the identification and extraction of salient characteristics or patterns, including edges, textures, shapes, and color attributes. Contemporary feature extractors predominantly leverage deep learning architectures, such as Convolutional Neural Networks (CNNs) and Vision Transformers (VITs). The availability of diverse feature extractors in the literature provides a wide range of feature representations. Features extracted from an image depend on the specific application, the chosen extractor, and its configuration. Therefore, integrating complementary information by combining distinct extractors offers a promising way to enhance performance. Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), have emerged as powerful and widely adopted approaches for semi-supervised image classification, as they effectively leverage both labeled and unlabeled data while exploiting the underlying graph structures that capture relationships among samples. This study proposes a novel approach for GNNs in scenarios where labeled data is scarce, by integrating diverse sets of feature and graph representations derived from various extractors in classification scenarios. Experimental investigations were conducted, encompassing combinations of distinct feature and graph extractors, as well as rank aggregation strategies. The primary contributions of this work are underscored by the experimental findings, which demonstrate that the strategic combination of feature and graph representations, coupled with the application of manifold learning for graph processing, leads to significant improvements in classification accuracy across the majority of experimental conditions. Furthermore, the utilization of rank aggregation techniques to integrate features from different extractors was shown to enhance classification accuracy. © 2026, Brazilian Computing Society. All rights reserved.},
keywords = {Classification accuracy, Convolution, Convolutional neural networks, extraction, Feature extraction, Feature extractor, Feature Fusion, Feature representation, Features fusions, Graph Neural Networks, Graph representation, Graph structures, Graph theory, Graphic methods, Image classification, Labeled data, Rank Aggregation, Semi-supervised, Semi-supervised image classification, Supervised image classifications, Textures},
pubstate = {published},
tppubtype = {article}
}
Feature extraction involves the identification and extraction of salient characteristics or patterns, including edges, textures, shapes, and color attributes. Contemporary feature extractors predominantly leverage deep learning architectures, such as Convolutional Neural Networks (CNNs) and Vision Transformers (VITs). The availability of diverse feature extractors in the literature provides a wide range of feature representations. Features extracted from an image depend on the specific application, the chosen extractor, and its configuration. Therefore, integrating complementary information by combining distinct extractors offers a promising way to enhance performance. Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), have emerged as powerful and widely adopted approaches for semi-supervised image classification, as they effectively leverage both labeled and unlabeled data while exploiting the underlying graph structures that capture relationships among samples. This study proposes a novel approach for GNNs in scenarios where labeled data is scarce, by integrating diverse sets of feature and graph representations derived from various extractors in classification scenarios. Experimental investigations were conducted, encompassing combinations of distinct feature and graph extractors, as well as rank aggregation strategies. The primary contributions of this work are underscored by the experimental findings, which demonstrate that the strategic combination of feature and graph representations, coupled with the application of manifold learning for graph processing, leads to significant improvements in classification accuracy across the majority of experimental conditions. Furthermore, the utilization of rank aggregation techniques to integrate features from different extractors was shown to enhance classification accuracy. © 2026, Brazilian Computing Society. All rights reserved.
4.
Sadallah, N.; Allili, M. S.
Crackforward: Context-Aware Severity-Stage Crack Synthesis for Data Augmentation Article d'actes
Dans: Can Conf Electr Comput Eng, p. 627–632, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 08407789 (ISSN); 979-833158840-3 (ISBN), (Journal Abbreviation: Can Conf Electr Comput Eng).
Résumé | Liens | BibTeX | Étiquettes: Context-Aware, Crack Expansion, Crack morphology, Crack propagation, Crack segmentation, Crack segmentations, Data augmentation, Growth patterns, Health monitoring, Local Eigenvector Orientation, Morphology, Random processes, Structural health, Structural health monitoring, Textures, Unet-style generation
@inproceedings{sadallahCrackforwardContextAwareSeverityStage2026,
title = {Crackforward: Context-Aware Severity-Stage Crack Synthesis for Data Augmentation},
author = {N. Sadallah and M. S. Allili},
url = {https://www.scopus.com/pages/publications/105046224944?origin=resultslist},
doi = {10.1109/CCECE68150.2026.11610144},
isbn = {08407789 (ISSN); 979-833158840-3 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Can Conf Electr Comput Eng},
pages = {627–632},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Reliable crack segmentation is vital for structural health monitoring, yet the scarcity of well-annotated data constitutes a major challenge. To address this limitation, we propose a novel context-aware generative framework designed to synthesize realistic crack growth patterns for data augmentation. Unlike existing methods that primarily manipulate textures or background content, CrackForward explicitly models crack morphology by combining directional crack elongation with learned thickening and branching. Our framework integrates two key innovations: (i) a contextually guided crack expansion module, which uses local directional cues and adaptive random walk to simulate realistic propagation paths; and (ii) a two-stage U-Net-style generator that learns to reproduce spatially varying crack characteristics such as thickness, branching, and growth. Experimental results show that the generated samples preserve target-stage saturation and thickness characteristics and improve the performance of several crack segmentation architectures. These results indicate that structure-aware synthetic crack generation can provide more informative training data than conventional augmentation alone. © 2026 IEEE.},
note = {Journal Abbreviation: Can Conf Electr Comput Eng},
keywords = {Context-Aware, Crack Expansion, Crack morphology, Crack propagation, Crack segmentation, Crack segmentations, Data augmentation, Growth patterns, Health monitoring, Local Eigenvector Orientation, Morphology, Random processes, Structural health, Structural health monitoring, Textures, Unet-style generation},
pubstate = {published},
tppubtype = {inproceedings}
}
Reliable crack segmentation is vital for structural health monitoring, yet the scarcity of well-annotated data constitutes a major challenge. To address this limitation, we propose a novel context-aware generative framework designed to synthesize realistic crack growth patterns for data augmentation. Unlike existing methods that primarily manipulate textures or background content, CrackForward explicitly models crack morphology by combining directional crack elongation with learned thickening and branching. Our framework integrates two key innovations: (i) a contextually guided crack expansion module, which uses local directional cues and adaptive random walk to simulate realistic propagation paths; and (ii) a two-stage U-Net-style generator that learns to reproduce spatially varying crack characteristics such as thickness, branching, and growth. Experimental results show that the generated samples preserve target-stage saturation and thickness characteristics and improve the performance of several crack segmentation architectures. These results indicate that structure-aware synthetic crack generation can provide more informative training data than conventional augmentation alone. © 2026 IEEE.
5.
Attoumi, H.; Tajeuna, E. G.; Allili, M. S.
Causal Graph Modeling of Network Traffic for Early Cyberattack Prediction Article d'actes
Dans: R., Al-Mallah; F., Cuppens; S., Ayed; F., Sailhan; J., Garcia-Alfaro (Ed.): Lect. Notes Comput. Sci., p. 167–183, Springer Science and Business Media Deutschland GmbH, 2026, ISBN: 03029743 (ISSN); 978-303220025-9 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Résumé | Liens | BibTeX | Étiquettes: Attack prediction, Bipartite graphs, Causal graph, Causal network, Co-evolving time series, Cyber-attacks, Forecasting, Graph model, Graph Neural Networks, Graph theory, Network traffic, Prediction models, Time series, Times series
@inproceedings{attoumiCausalGraphModeling2026,
title = {Causal Graph Modeling of Network Traffic for Early Cyberattack Prediction},
author = {H. Attoumi and E. G. Tajeuna and M. S. Allili},
editor = {Al-Mallah R. and Cuppens F. and Ayed S. and Sailhan F. and Garcia-Alfaro J.},
url = {https://www.scopus.com/pages/publications/105040543052?origin=resultslist},
doi = {10.1007/978-3-032-20026-6_10},
isbn = {03029743 (ISSN); 978-303220025-9 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {16403 LNCS},
pages = {167–183},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {We propose a causal-graph forecasting framework for early cyberattack prediction that integrates causal network modeling with dynamic bipartite graph–based time series forecasting. Our method captures both the cause–effect structure of network traffic and the co-evolving, non-stationary dynamics of attack-related signals. Network entities are represented as nodes, while edges encode directional causal influences inferred from traffic flows. To model evolving dependencies, we approximate each traffic series over contiguous intervals with parametric functions and construct temporal bipartite graphs that link these models to time series, revealing shared latent patterns and context-driven relationships. This hybrid representation enables adaptive model selection and accurate forecasting of malicious behavior before it escalates. Experiments on benchmark of network traces show that our approach delivers early, interpretable, and highly accurate cyberattack predictions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Attack prediction, Bipartite graphs, Causal graph, Causal network, Co-evolving time series, Cyber-attacks, Forecasting, Graph model, Graph Neural Networks, Graph theory, Network traffic, Prediction models, Time series, Times series},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a causal-graph forecasting framework for early cyberattack prediction that integrates causal network modeling with dynamic bipartite graph–based time series forecasting. Our method captures both the cause–effect structure of network traffic and the co-evolving, non-stationary dynamics of attack-related signals. Network entities are represented as nodes, while edges encode directional causal influences inferred from traffic flows. To model evolving dependencies, we approximate each traffic series over contiguous intervals with parametric functions and construct temporal bipartite graphs that link these models to time series, revealing shared latent patterns and context-driven relationships. This hybrid representation enables adaptive model selection and accurate forecasting of malicious behavior before it escalates. Experiments on benchmark of network traces show that our approach delivers early, interpretable, and highly accurate cyberattack predictions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
6.
Nouboukpo, A.; Allili, M. S.
Semi-supervised flow-augmented Gaussian mixture VAEs with weighted contrastive learning for out-of-distribution detection Article de journal
Dans: Knowledge-Based Systems, vol. 347, 2026, ISSN: 09507051 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Auto encoders, Benchmarking, Classification (of information), Clusterings, Contrastive Learning, Flow based, Flow-based GMM clustering, Gaussian distribution, Gaussian-mixtures, Learning algorithms, Learning systems, Out-of-distribution detection, Semantics, Semi-supervised, Semi-supervised learning, uncertainty, Variational autoencoder, Variational autoencoders (VAEs)
@article{nouboukpoSemisupervisedFlowaugmentedGaussian2026,
title = {Semi-supervised flow-augmented Gaussian mixture VAEs with weighted contrastive learning for out-of-distribution detection},
author = {A. Nouboukpo and M. S. Allili},
url = {https://www.scopus.com/pages/publications/105040505017?origin=resultslist},
doi = {10.1016/j.knosys.2026.116312},
issn = {09507051 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Knowledge-Based Systems},
volume = {347},
publisher = {Elsevier B.V.},
abstract = {Out-of-distribution (OOD) detection is essential for the safe deployment of machine learning systems, particularly in high-stakes domains where identifying inputs that deviate from the in-distribution (ID) is critical. However, in real-world scenarios, true OOD data are typically unavailable at training time, and only auxiliary datasets acting as imperfect proxies can be used. This makes fully supervised approaches impractical and potentially biased toward specific anomaly types. To address this limitation, we propose FLoW-ssGMVAE, a semi-supervised generative–discriminative framework that leverages limited proxy anomaly supervision together with abundant unlabeled data for robust OOD detection. Our model integrates a flow-augmented Gaussian Mixture VAE, enabling flexible latent modeling that captures nonlinear and multimodal intra-class distributions. Unlike standard VAEs with unimodal Gaussian priors, it uses class-conditional normalizing flows to better represent complex ID and OOD variability. To further refine the latent space and strengthen ID/OOD separation, our model integrates a weighted contrastive learning objective guided by sample-wise attention scores derived from likelihood uncertainty. This mechanism emphasizes ambiguous or hard-to-classify instances, reinforcing semantic boundaries and reducing false detections. By combining expressive generative modeling with uncertainty-aware discriminative training, our method constructs a structured and interpretable latent space that reliably identifies OOD samples under limited supervision. Experiments on standard benchmarks validate the effectiveness and scalability of our approach, demonstrating state-of-the-art performance with reasonable computational cost. Our implementation will be available at: FLoW-ssGMVAE. © 2026},
keywords = {Auto encoders, Benchmarking, Classification (of information), Clusterings, Contrastive Learning, Flow based, Flow-based GMM clustering, Gaussian distribution, Gaussian-mixtures, Learning algorithms, Learning systems, Out-of-distribution detection, Semantics, Semi-supervised, Semi-supervised learning, uncertainty, Variational autoencoder, Variational autoencoders (VAEs)},
pubstate = {published},
tppubtype = {article}
}
Out-of-distribution (OOD) detection is essential for the safe deployment of machine learning systems, particularly in high-stakes domains where identifying inputs that deviate from the in-distribution (ID) is critical. However, in real-world scenarios, true OOD data are typically unavailable at training time, and only auxiliary datasets acting as imperfect proxies can be used. This makes fully supervised approaches impractical and potentially biased toward specific anomaly types. To address this limitation, we propose FLoW-ssGMVAE, a semi-supervised generative–discriminative framework that leverages limited proxy anomaly supervision together with abundant unlabeled data for robust OOD detection. Our model integrates a flow-augmented Gaussian Mixture VAE, enabling flexible latent modeling that captures nonlinear and multimodal intra-class distributions. Unlike standard VAEs with unimodal Gaussian priors, it uses class-conditional normalizing flows to better represent complex ID and OOD variability. To further refine the latent space and strengthen ID/OOD separation, our model integrates a weighted contrastive learning objective guided by sample-wise attention scores derived from likelihood uncertainty. This mechanism emphasizes ambiguous or hard-to-classify instances, reinforcing semantic boundaries and reducing false detections. By combining expressive generative modeling with uncertainty-aware discriminative training, our method constructs a structured and interpretable latent space that reliably identifies OOD samples under limited supervision. Experiments on standard benchmarks validate the effectiveness and scalability of our approach, demonstrating state-of-the-art performance with reasonable computational cost. Our implementation will be available at: FLoW-ssGMVAE. © 2026
7.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks Article de journal
Dans: IEEE Transactions on Artificial Intelligence, vol. 7, no 6, p. 3146–3159, 2026, ISSN: 26914581 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Aerial vehicle, Air navigation, Aircraft landing, Antennas, Automatic uncrewed aerial vehicle (UAV) navigation, Automatic unmanned aerial vehicle navigation, Autonomous navigation, Behavioral research, Computer vision, decision making, Deep learning, deep ordinal regression, Emergency services, Landing zones, Learning systems, Ordinal regression, regression analysis, Safe landing, Safe landing zone, safe landing zones (SLZs), Semantic segmentation, Semantics, Textures, Three dimensional computer graphics, Unmanned aerial vehicles (UAV), Vehicle navigation, Visual servoing
@article{abdollahzadehVisualSafetyMapping2026,
title = {Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/pages/publications/105023324811?origin=resultslist},
doi = {10.1109/TAI.2025.3635093},
issn = {26914581 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Transactions on Artificial Intelligence},
volume = {7},
number = {6},
pages = {3146–3159},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {As uncrewed aerial vehicles (UAVs) see growing use in civilian applications, reliably identifying safe landing zones (SLZs) in varied environments is essential for autonomous navigation and emergency response. Passive vision sensors offer a low cost, lightweight solution for real-time terrain analysis and 3D scene reconstruction, making them ideal for onboard systems. We introduce ordinal regression safe landing zones netwrok (OR-SLZNet), an original deep learning model based on ordinal regression to predict SLZs from UAV imagery. Unlike prior approaches, OR-SLZNet produces dense, multilevel safety maps by jointly leveraging photometric (e.g., color and texture) and geometric cues (e.g., flatness, slope, and depth), assigning each pixel an ordinal safety score that reflects landing suitability. With real-time inference (textbackslashsim0.02 s/frame), the model supports onboard deployment and rapid decision-making in time-critical situations. Extensive experiments on five diverse datasets demonstrate OR-SLZNet effectiveness and strong generalization across a wide range of structural complexities. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft landing, Antennas, Automatic uncrewed aerial vehicle (UAV) navigation, Automatic unmanned aerial vehicle navigation, Autonomous navigation, Behavioral research, Computer vision, decision making, Deep learning, deep ordinal regression, Emergency services, Landing zones, Learning systems, Ordinal regression, regression analysis, Safe landing, Safe landing zone, safe landing zones (SLZs), Semantic segmentation, Semantics, Textures, Three dimensional computer graphics, Unmanned aerial vehicles (UAV), Vehicle navigation, Visual servoing},
pubstate = {published},
tppubtype = {article}
}
As uncrewed aerial vehicles (UAVs) see growing use in civilian applications, reliably identifying safe landing zones (SLZs) in varied environments is essential for autonomous navigation and emergency response. Passive vision sensors offer a low cost, lightweight solution for real-time terrain analysis and 3D scene reconstruction, making them ideal for onboard systems. We introduce ordinal regression safe landing zones netwrok (OR-SLZNet), an original deep learning model based on ordinal regression to predict SLZs from UAV imagery. Unlike prior approaches, OR-SLZNet produces dense, multilevel safety maps by jointly leveraging photometric (e.g., color and texture) and geometric cues (e.g., flatness, slope, and depth), assigning each pixel an ordinal safety score that reflects landing suitability. With real-time inference (textbackslashsim0.02 s/frame), the model supports onboard deployment and rapid decision-making in time-critical situations. Extensive experiments on five diverse datasets demonstrate OR-SLZNet effectiveness and strong generalization across a wide range of structural complexities. © 2020 IEEE.
8.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments Article de journal
Dans: IEEE Open Journal of the Computer Society, vol. 7, p. 492–503, 2026, ISSN: 26441268 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)
@article{abdollahzadehVisionBasedFrameworkSafe2026,
title = {A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/pages/publications/105029942397?origin=resultslist},
doi = {10.1109/OJCS.2026.3663268},
issn = {26441268 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Open Journal of the Computer Society},
volume = {7},
pages = {492–503},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},
pubstate = {published},
tppubtype = {article}
}
Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.
9.
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.
10.
Abdollahzadeh, S.; Allili, M. S.; Boulmerka, A.; Lapointe, J. -F.
A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments Article de journal
Dans: IEEE Open Journal of the Computer Society, vol. 7, p. 492–503, 2026, ISSN: 26441268 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)
@article{abdollahzadehVisionBasedFrameworkSafe2026a,
title = {A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments},
author = {S. Abdollahzadeh and M. S. Allili and A. Boulmerka and J. -F. Lapointe},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105029942397&doi=10.1109%2FOJCS.2026.3663268&partnerID=40&md5=b11484e035458c84b1d3f6780b92c91c},
doi = {10.1109/OJCS.2026.3663268},
issn = {26441268 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Open Journal of the Computer Society},
volume = {7},
pages = {492–503},
abstract = {Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.},
keywords = {Aerial vehicle, Air navigation, Aircraft detection, Aircraft landing, Antennas, automatic UAV navigation, Computer vision, Dynamic environments, Forecasting, Homographies, Landing zones, Learning systems, Motion tracking, Object detection, Object recognition, Object Tracking, object trajectory prediction, Robotics, Safe landing, Safe landing zone, safe landing zones (SLZ), Semantic segmentation, Semantics, Trajectories, Trajectory forecasting, Uncrewed aerial vehicles (UAVs), Unmanned aerial vehicle, Unmanned aerial vehicles (UAV)},
pubstate = {published},
tppubtype = {article}
}
Identification safe landing zones (SLZ) for Uncrewed Aerial Vehicles (UAVs) is important to ensure reliable and safe navigation, especially when they are operated in complex and safety-critical environments. However, this is a challenging task due to obstacles and UAV motion. This paper proposes a vision-based framework that maps SLZs in dynamic scenes by integrating several functionalities for analyzing visually static and dynamic aspects of a scene. Static analysis is achieved through context-aware segmentation which divides the image into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass). For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder–decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation. © 2020 IEEE.