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24 results on '"Carletti, Vincenzo"'

2. Detecting Abnormal Communication Patterns in IoT Networks Using Graph Neural Networks

Author

Carletti, Vincenzo, Foggia, Pasquale, Vento, Mario, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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6. Facial Soft-biometrics Obfuscation through Adversarial Attacks.

Author

Carletti, Vincenzo, Foggia, Pasquale, Greco, Antonio, Saggese, Alessia, and Vento, Mario

Subjects
CONVOLUTIONAL neural networks, MACHINE learning, SOCIAL networks, BIOMETRY, PRIVACY
Abstract

Sharing facial pictures through online services, especially on social networks, has become a common habit for thousands of users. This practice hides a possible threat to privacy: the owners of such services, as well as malicious users, could automatically extract information from faces using modern and effective neural networks. In this article, we propose the harmless use of adversarial attacks, i.e., variations of images that are almost imperceptible to the human eye and that are typically generated with the malicious purpose to mislead Convolutional Neural Networks (CNNs). Such attacks have been instead adopted to (1) obfuscate soft biometrics (gender, age, ethnicity) but (2) without degrading the quality of the face images posted online. We achieve the above-mentioned two conflicting goals by modifying the implementations of four of the most popular adversarial attacks, namely FGSM, PGD, DeepFool, and C&W, in order to constrain the average amount of noise they generate on the image and the maximum perturbation they add on the single pixel. We demonstrate, in an experimental framework including three popular CNNs, namely VGG16, SENet, and MobileNetV3, that the considered obfuscation method, which requires at most 4 seconds for each image, is effective not only when we have a complete knowledge of the neural network that extracts the soft biometrics (white box attacks) but also when the adversarial attacks are generated in a more realistic black box scenario. Finally, we prove that an opponent can implement defense techniques to partially reduce the effect of the obfuscation, but substantially paying in terms of accuracy over clean images; this result, confirmed by the experiments carried out with three popular defense methods, namely adversarial training, denoising autoencoder, and Kullback-Leibler autoencoder, shows that it is not convenient for the opponent to defend himself and that the proposed approach is robust to defenses. [ABSTRACT FROM AUTHOR]

Published
2024
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7. A Smart Visual Sensor for Smoke Detection Based on Deep Neural Networks.

Author

Carletti, Vincenzo, Greco, Antonio, Saggese, Alessia, and Vento, Bruno

Subjects
*ARTIFICIAL neural networks, *CONVOLUTIONAL neural networks, *INTELLIGENT sensors, *SMOKE, *COMPUTER vision, *MOTION analysis
Abstract

The automatic detection of smoke by analyzing the video stream acquired by traditional surveillance cameras is becoming a more and more interesting problem for the scientific community thanks to the necessity to prevent fires at the very early stages. The adoption of a smart visual sensor, namely a computer vision algorithm running in real time, allows one to overcome the limitations of standard physical sensors. Nevertheless, this is a very challenging problem, due to the strong similarity of the smoke with other environmental elements like clouds, fog and dust. In addition to this challenge, data available for training deep neural networks is limited and not fully representative of real environments. Within this context, in this paper we propose a new method for smoke detection based on the combination of motion and appearance analysis with a modern convolutional neural network (CNN). Moreover, we propose a new dataset, called the MIVIA Smoke Detection Dataset (MIVIA-SDD), publicly available for research purposes; it consists of 129 videos covering about 28 h of recordings. The proposed hybrid method, trained and evaluated on the proposed dataset, demonstrated to be very effective by achieving a 94% smoke recognition rate and, at the same time, a substantially lower false positive rate if compared with fully deep learning-based approaches (14% vs. 100%). Therefore, the proposed combination of motion and appearance analysis with deep learning CNNs can be further investigated to improve the precision of fire detection approaches. [ABSTRACT FROM AUTHOR]

Published
2024
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8. An intelligent flying system for automatic detection of faults in photovoltaic plants

Author

Carletti, Vincenzo, Greco, Antonio, Saggese, Alessia, and Vento, Mario

Abstract

For several years, fault diagnosis of photovoltaic (PV) plants has been manually performed by the human operator by a visual inspection or automatically, by evaluating electrical measures collected by sensors mounted on each PV module. In recent years, a notable interest of the scientific community has been devoted towards the definition of algorithms able to automatically analyse the sequence of images acquired by a thermal camera mounted on board of an unmanned aerial vehicle (UAV) for early PV anomaly detection. In this paper, we define a model-based approach for the detection of the panels, which uses the structural regularity of the PV string and a novel technique for local hot spot detection, based on the use of a fast and effective algorithm for finding local maxima in the PV panel region. Finally, we introduce the concept of global hot spot detection, namely a multi-frame recognition of PV faults which further improves the anomaly detection accuracy of the proposed method. The algorithm has been designed and optimized so as to run in real-time directly on an embedded system on board of the UAV. The accuracy of the proposed approach has been experimented on several video sequences with a standard protocol in terms of Precision, Recall and F-Score, so that our dataset and our quantitative results can be used for future comparisons and to evaluate the reliability of computer vision techniques designed for thermographic PV inspection.

Published
2024
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9. An effective real time gender recognition system for smart cameras

Author

Carletti, Vincenzo, Greco, Antonio, Saggese, Alessia, and Vento, Mario

Abstract

In recent years we have assisted to a growing interest for embedded vision, due to the availability of low cost hardware systems, effective for energy consumption, flexible for their size at the cost of limited (compared to the server) computing resources. Their use is boosted by the simplicity of their positioning in places where energy or network bandwidth is limited. Smart cameras are digital cameras embedding computer systems able to host video applications; due to the cost and the performance, they are progressively gaining popularity and conquering large amount of the market. Smart cameras are now able to host on board video applications, even if this imposes an heavy reformulation of the algorithms and of the software design so as to make them compliant with the limited CPUs and the small RAM and flash memory (typically of a few megabytes). In this paper we propose a method for gender recognition on video sequences, specifically designed for making it suited to smart cameras; although the algorithm uses very limited resources (in terms of RAM and CPU), it is able to run on smart cameras available today, presenting at the same time an high accuracy on unrestricted videos taken in real environments (malls, shops, etc.).

Published
2024
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10. Graph-Based Deep Learning on the Swiss River Network

Author

Fankhauser, Benjamin, Bigler, Vidushi, Riesen, Kaspar, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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11. Matching-Graphs for Building Classification Ensembles

Author

Fuchs, Mathias, Riesen, Kaspar, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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12. GNN-DES: A New End-to-End Dynamic Ensemble Selection Method Based on Multi-label Graph Neural Network

Author

de Araujo Souza, Mariana, Sabourin, Robert, da Cunha Cavalcanti, George Darmiton, e Cruz, Rafael Menelau Oliveira, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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13. Reducing the Computational Complexity of the Eccentricity Transform of a Tree

Author

Banaeyan, Majid, Kropatsch, Walter G., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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14. C2N-ABDP: Cluster-to-Node Attention-Based Differentiable Pooling

Author

Ye, Rongji, Cui, Lixin, Rossi, Luca, Wang, Yue, Xu, Zhuo, Bai, Lu, Hancock, Edwin R., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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15. Graph-Based Representation for Multi-image Super-Resolution

Author

Tarasiewicz, Tomasz, Kawulok, Michal, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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16. Cell Segmentation of in situ Transcriptomics Data Using Signed Graph Partitioning

Author

Andersson, Axel, Behanova, Andrea, Wählby, Carolina, Malmberg, Filip, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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17. Graph Normalizing Flows to Pre-image Free Machine Learning for Regression

Author

Glédel, Clément, Gaüzère, Benoît, Honeine, Paul, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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18. Graph-Based vs. Vector-Based Classification: A Fair Comparison

Author

Gillioz, Anthony, Riesen, Kaspar, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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19. Maximal Independent Sets for Pooling in Graph Neural Networks

Author

Stanovic, Stevan, Gaüzère, Benoit, Brun, Luc, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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20. Splitting Structural and Semantic Knowledge in Graph Autoencoders for Graph Regression

Author

Fadlallah, Sarah, Segura Alabart, Natália, Julià, Carme, Serratosa, Francesc, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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21. An Efficient Entropy-Based Graph Kernel

Author

Ourdjini, Aymen, Kiouche, Abd Errahmane, Seba, Hamida, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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22. Minimum Spanning Set Selection in Graph Kernels

Author

Tortorella, Domenico, Micheli, Alessio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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23. A Practical Algorithm for Max-Norm Optimal Binary Labeling of Graphs

Author

Malmberg, Filip, Falcão, Alexandre X., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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24. Quadratic Kernel Learning for Interpolation Kernel Machine Based Graph Classification

Author

Zhang, Jiaqi, Liu, Cheng-Lin, Jiang, Xiaoyi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, Foggia, Pasquale, editor, Conte, Donatello, editor, and Carletti, Vincenzo, editor

Published
2023
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