Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
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.
2.
Valem, L. P.; Pedronette, D. C. G.; Allili, M. S.
Contrastive Loss Based on Contextual Similarity for Image Classification Article d'actes
Dans: G., Bebis; V., Patel; J., Gu; J., Panetta; Y., Gingold; K., Johnsen; M.S., Arefin; S., Dutta; A., Biswas (Ed.): Lect. Notes Comput. Sci., p. 58–69, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303177391-4 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Résumé | Liens | BibTeX | Étiquettes: Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure
@inproceedings{valem_contrastive_2025,
title = {Contrastive Loss Based on Contextual Similarity for Image Classification},
author = {L. P. Valem and D. C. G. Pedronette and M. S. Allili},
editor = {Bebis G. and Patel V. and Gu J. and Panetta J. and Gingold Y. and Johnsen K. and Arefin M.S. and Dutta S. and Biswas A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218461565&doi=10.1007%2f978-3-031-77392-1_5&partnerID=40&md5=cf885303646c3b1a4f4eacb87d02a2b6},
doi = {10.1007/978-3-031-77392-1_5},
isbn = {03029743 (ISSN); 978-303177391-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15046 LNCS},
pages = {58–69},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure},
pubstate = {published},
tppubtype = {inproceedings}
}
Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
3.
Valem, L. P.; Pedronette, D. C. G.; Allili, M. S.
Contrastive Loss Based on Contextual Similarity for Image Classification Article d'actes
Dans: G., Bebis; V., Patel; J., Gu; J., Panetta; Y., Gingold; K., Johnsen; M.S., Arefin; S., Dutta; A., Biswas (Ed.): Lect. Notes Comput. Sci., p. 58–69, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303177391-4 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Résumé | Liens | BibTeX | Étiquettes: Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure
@inproceedings{valemContrastiveLossBased2025a,
title = {Contrastive Loss Based on Contextual Similarity for Image Classification},
author = {L. P. Valem and D. C. G. Pedronette and M. S. Allili},
editor = {Bebis G. and Patel V. and Gu J. and Panetta J. and Gingold Y. and Johnsen K. and Arefin M.S. and Dutta S. and Biswas A.},
url = {https://www.scopus.com/pages/publications/85218461565?origin=resultslist},
doi = {10.1007/978-3-031-77392-1_5},
isbn = {03029743 (ISSN); 978-303177391-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15046 LNCS},
pages = {58–69},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure},
pubstate = {published},
tppubtype = {inproceedings}
}
Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
4.
Valem, L. P.; Pedronette, D. C. G.; Allili, M. S.
Contrastive Loss Based on Contextual Similarity for Image Classification Article d'actes
Dans: G., Bebis; V., Patel; J., Gu; J., Panetta; Y., Gingold; K., Johnsen; M.S., Arefin; S., Dutta; A., Biswas (Ed.): Lect. Notes Comput. Sci., p. 58–69, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303177391-4 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Résumé | Liens | BibTeX | Étiquettes: Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure
@inproceedings{valemContrastiveLossBased2025,
title = {Contrastive Loss Based on Contextual Similarity for Image Classification},
author = {L. P. Valem and D. C. G. Pedronette and M. S. Allili},
editor = {Bebis G. and Patel V. and Gu J. and Panetta J. and Gingold Y. and Johnsen K. and Arefin M.S. and Dutta S. and Biswas A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218461565&doi=10.1007%2f978-3-031-77392-1_5&partnerID=40&md5=cf885303646c3b1a4f4eacb87d02a2b6},
doi = {10.1007/978-3-031-77392-1_5},
isbn = {03029743 (ISSN); 978-303177391-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15046 LNCS},
pages = {58–69},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure},
pubstate = {published},
tppubtype = {inproceedings}
}
Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
5.
Renaud, P.; Trottier, D.; Rouleau, J. -L.; Goyette, M.; Saumur, C.; Boukhalfi, T.; Bouchard, S.
Using immersive virtual reality and anatomically correct computer-generated characters in the forensic assessment of deviant sexual preferences Article de journal
Dans: Virtual Reality, vol. 18, no 1, p. 37–47, 2014, ISSN: 13594338, (Publisher: Springer London).
Résumé | Liens | BibTeX | Étiquettes: Area Under the Curve (AUC), Classification accuracy, Computer forensics, Computer generated characters, Deregulation, Gears, Immersive virtual reality, Pedophilia, Plethysmography, Receiver operating characteristic analysis, Self regulation, Virtual addresses, Virtual character, virtual reality
@article{renaud_using_2014,
title = {Using immersive virtual reality and anatomically correct computer-generated characters in the forensic assessment of deviant sexual preferences},
author = {P. Renaud and D. Trottier and J. -L. Rouleau and M. Goyette and C. Saumur and T. Boukhalfi and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893969900&doi=10.1007%2fs10055-013-0235-8&partnerID=40&md5=847ee510fd6f3c30ec6285071e0df167},
doi = {10.1007/s10055-013-0235-8},
issn = {13594338},
year = {2014},
date = {2014-01-01},
journal = {Virtual Reality},
volume = {18},
number = {1},
pages = {37–47},
publisher = {Springer London},
abstract = {Penile plethysmography (PPG) is the gold standard for the assessment of sexual interests, especially among sex offenders of children. Nonetheless, this method faces some ethical limitations inherent to the nature of its stimuli and could benefit from the improvement of its ecological validity. The use of computer-generated characters (CGC) in virtual immersion for PPG assessment might help address these issues. A new application developed to design made-to-measure anatomically correct virtual characters compatible with the Tanner developmental stages is presented. The main purpose of this study was to determine how the virtual reality (VR) modality compares to the standard auditory modality on their capacity to generate sexual arousal profiles and deviance differentials indicative of sexual interests. The erectile responses of 22 sex offenders of children and 42 non-deviant adult males were recorded. While both stimulus modalities generated significantly different genital arousal profiles for sex offenders of children and non-deviant males, deviance differentials calculated from the VR modality allowed for significantly higher classification accuracy. Performing receiver operating characteristic analyses further assessed discriminant potential. Auditory modality yielded an area under the curve (AUC) of 0.79 (SE = 0.059) while CGC in VR yielded an AUC of 0.90 (SE = 0.052). Overall, results suggest that the VR modality allows significantly better group classification accuracy and discriminant validity than audio stimuli, which provide empirical support for the use of this new method for PPG assessment. Additionally, the potential use of VR in interventions pertaining to self-regulation of sexual offending is addressed in conclusion. © 2013 Springer-Verlag London.},
note = {Publisher: Springer London},
keywords = {Area Under the Curve (AUC), Classification accuracy, Computer forensics, Computer generated characters, Deregulation, Gears, Immersive virtual reality, Pedophilia, Plethysmography, Receiver operating characteristic analysis, Self regulation, Virtual addresses, Virtual character, virtual reality},
pubstate = {published},
tppubtype = {article}
}
Penile plethysmography (PPG) is the gold standard for the assessment of sexual interests, especially among sex offenders of children. Nonetheless, this method faces some ethical limitations inherent to the nature of its stimuli and could benefit from the improvement of its ecological validity. The use of computer-generated characters (CGC) in virtual immersion for PPG assessment might help address these issues. A new application developed to design made-to-measure anatomically correct virtual characters compatible with the Tanner developmental stages is presented. The main purpose of this study was to determine how the virtual reality (VR) modality compares to the standard auditory modality on their capacity to generate sexual arousal profiles and deviance differentials indicative of sexual interests. The erectile responses of 22 sex offenders of children and 42 non-deviant adult males were recorded. While both stimulus modalities generated significantly different genital arousal profiles for sex offenders of children and non-deviant males, deviance differentials calculated from the VR modality allowed for significantly higher classification accuracy. Performing receiver operating characteristic analyses further assessed discriminant potential. Auditory modality yielded an area under the curve (AUC) of 0.79 (SE = 0.059) while CGC in VR yielded an AUC of 0.90 (SE = 0.052). Overall, results suggest that the VR modality allows significantly better group classification accuracy and discriminant validity than audio stimuli, which provide empirical support for the use of this new method for PPG assessment. Additionally, the potential use of VR in interventions pertaining to self-regulation of sexual offending is addressed in conclusion. © 2013 Springer-Verlag London.
6.
Renaud, P.; Trottier, D.; Rouleau, J. -L.; Goyette, M.; Saumur, C.; Boukhalfi, T.; Bouchard, S.
Using immersive virtual reality and anatomically correct computer-generated characters in the forensic assessment of deviant sexual preferences Article de journal
Dans: Virtual Reality, vol. 18, no 1, p. 37–47, 2014, ISSN: 13594338.
Résumé | Liens | BibTeX | Étiquettes: Area Under the Curve (AUC), Classification accuracy, Computer forensics, Computer generated characters, Deregulation, Gears, Immersive virtual reality, Pedophilia, Plethysmography, Receiver operating characteristic analysis, Self regulation, Virtual addresses, Virtual character, virtual reality
@article{renaudUsingImmersiveVirtual2014,
title = {Using immersive virtual reality and anatomically correct computer-generated characters in the forensic assessment of deviant sexual preferences},
author = {P. Renaud and D. Trottier and J. -L. Rouleau and M. Goyette and C. Saumur and T. Boukhalfi and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893969900&doi=10.1007%2fs10055-013-0235-8&partnerID=40&md5=847ee510fd6f3c30ec6285071e0df167},
doi = {10.1007/s10055-013-0235-8},
issn = {13594338},
year = {2014},
date = {2014-01-01},
journal = {Virtual Reality},
volume = {18},
number = {1},
pages = {37–47},
publisher = {Springer London},
abstract = {Penile plethysmography (PPG) is the gold standard for the assessment of sexual interests, especially among sex offenders of children. Nonetheless, this method faces some ethical limitations inherent to the nature of its stimuli and could benefit from the improvement of its ecological validity. The use of computer-generated characters (CGC) in virtual immersion for PPG assessment might help address these issues. A new application developed to design made-to-measure anatomically correct virtual characters compatible with the Tanner developmental stages is presented. The main purpose of this study was to determine how the virtual reality (VR) modality compares to the standard auditory modality on their capacity to generate sexual arousal profiles and deviance differentials indicative of sexual interests. The erectile responses of 22 sex offenders of children and 42 non-deviant adult males were recorded. While both stimulus modalities generated significantly different genital arousal profiles for sex offenders of children and non-deviant males, deviance differentials calculated from the VR modality allowed for significantly higher classification accuracy. Performing receiver operating characteristic analyses further assessed discriminant potential. Auditory modality yielded an area under the curve (AUC) of 0.79 (SE = 0.059) while CGC in VR yielded an AUC of 0.90 (SE = 0.052). Overall, results suggest that the VR modality allows significantly better group classification accuracy and discriminant validity than audio stimuli, which provide empirical support for the use of this new method for PPG assessment. Additionally, the potential use of VR in interventions pertaining to self-regulation of sexual offending is addressed in conclusion. © 2013 Springer-Verlag London.},
keywords = {Area Under the Curve (AUC), Classification accuracy, Computer forensics, Computer generated characters, Deregulation, Gears, Immersive virtual reality, Pedophilia, Plethysmography, Receiver operating characteristic analysis, Self regulation, Virtual addresses, Virtual character, virtual reality},
pubstate = {published},
tppubtype = {article}
}
Penile plethysmography (PPG) is the gold standard for the assessment of sexual interests, especially among sex offenders of children. Nonetheless, this method faces some ethical limitations inherent to the nature of its stimuli and could benefit from the improvement of its ecological validity. The use of computer-generated characters (CGC) in virtual immersion for PPG assessment might help address these issues. A new application developed to design made-to-measure anatomically correct virtual characters compatible with the Tanner developmental stages is presented. The main purpose of this study was to determine how the virtual reality (VR) modality compares to the standard auditory modality on their capacity to generate sexual arousal profiles and deviance differentials indicative of sexual interests. The erectile responses of 22 sex offenders of children and 42 non-deviant adult males were recorded. While both stimulus modalities generated significantly different genital arousal profiles for sex offenders of children and non-deviant males, deviance differentials calculated from the VR modality allowed for significantly higher classification accuracy. Performing receiver operating characteristic analyses further assessed discriminant potential. Auditory modality yielded an area under the curve (AUC) of 0.79 (SE = 0.059) while CGC in VR yielded an AUC of 0.90 (SE = 0.052). Overall, results suggest that the VR modality allows significantly better group classification accuracy and discriminant validity than audio stimuli, which provide empirical support for the use of this new method for PPG assessment. Additionally, the potential use of VR in interventions pertaining to self-regulation of sexual offending is addressed in conclusion. © 2013 Springer-Verlag London.