Publications

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D. - T. Dang-Nguyen, Piras, L., Giacinto, G., Boato, G., and De Natale, F. G. B., Retrieval of Diverse Images by Pre-filtering and Hierarchical Clustering, in MediaEval (Online Working Notes), Barcelona; Spain, 2014, vol. 1263. (221.52 KB)
D. - T. Dang-Nguyen, Piras, L., Giacinto, G., Boato, G., and De Natale, F. G. B., Multimodal Retrieval with Diversification and Relevance Feedback for Tourist Attraction Images, ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 13, no. 4, 2017. (5.94 MB)
D. - T. Dang-Nguyen, Piras, L., Riegler, M., Zhou, L., Lux, M., Tran, M. - T., Le, T. - K., Ninh, V. - T., and Gurrin, C., Overview of ImageCLEFlifelog 2019: Solve My Life Puzzle and Lifelog Moment Retrieval, in Working Notes of {CLEF} 2019 - Conference and Labs of the Evaluation Forum, Lugano, Switzerland, September 9-12, 2019., 2019. (4.58 MB)
D. - T. Dang-Nguyen, Piras, L., Giacinto, G., Boato, G., and De Natale, F. G. B., A Hybrid Approach for Retrieving Diverse Social Images of Landmarks, in IEEE International Conference on Multimedia & Expo (ICME), Torino, 2015. (1.16 MB)
S. Kumar Dash, Suarez-Tangil, G., Khan, S., Tam, K., Ahmadi, M., Kinder, J., and Cavallaro, L., DroidScribe: Classifying Android Malware Based on Runtime Behavior, in Mobile Security Technologies (MoST 2016), 2016. (571.22 KB)
R. Delussu, Putzu, L., and Fumera, G., An Empirical Evaluation of Cross-scene Crowd Counting Performance, in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISAPP, Valletta - Malta, 2020, vol. 4, pp. 373-380. (527.29 KB)
R. Delussu, Putzu, L., and Fumera, G., Investigating Synthetic Data Sets for Crowd Counting in Cross-scene Scenarios, in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISAPP 2020, Valletta - Malta, 2020, vol. 4, pp. 365-372. (4.23 MB)
R. Delussu, Putzu, L., and Fumera, G., Scene-specific Crowd Counting Using Synthetic Training Images, Pattern Recognition, vol. 124, 2022. (3.14 MB)
R. Delussu, Putzu, L., and Fumera, G., On the Effectiveness of Synthetic Data Sets for Training Person Re-identification Models, in Proceedings - International Conference on Pattern Recognition, 2022, vol. 2022-August, pp. 1208 – 1214.
R. Delussu, Putzu, L., Fumera, G., and Roli, F., Online Domain Adaptation for Person Re-Identification with a Human in the Loop, in 25th International Conference on Pattern Recognition, {ICPR} 2020, Virtual Event / Milan, Italy, January 10-15, 2021, 2020, pp. 3829–3836. (770.02 KB)
L. Demetrio, Biggio, B., Lagorio, G., Roli, F., and Armando, A., Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware, IEEE Transactions on Information Forensics and Security, vol. 16, pp. 3469-3478, 2021.
L. Demetrio, Biggio, B., Lagorio, G., Roli, F., and Armando, A., Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries, in 3rd Italian Conference on Cyber Security, ITASEC 2019, Pisa, Italy, 2019, vol. 2315. (801.85 KB)
L. Demetrio, Coull, S. E., Biggio, B., Lagorio, G., Armando, A., and Roli, F., Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection, ACM Trans. Priv. Secur., vol. 24, 2021.
A. Demontis, Melis, M., Biggio, B., Maiorca, D., Arp, D., Rieck, K., Corona, I., Giacinto, G., and Roli, F., Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection, IEEE Trans. Dependable and Secure Computing, vol. 16, no. 4, pp. 711-724, 2019. (3.61 MB)
A. Demontis, Russu, P., Biggio, B., Fumera, G., and Roli, F., On Security and Sparsity of Linear Classifiers for Adversarial Settings, in Joint IAPR Int'l Workshop on Structural, Syntactic, and Statistical Pattern Recognition, Merida, Mexico, 2016, vol. 10029 of LNCS, pp. 322-332. (425.68 KB)
A. Demontis, Biggio, B., Fumera, G., Giacinto, G., and Roli, F., Infinity-norm Support Vector Machines against Adversarial Label Contamination, 1st Italian Conference on CyberSecurity (ITASEC). Venice, Italy , pp. 106-115, 2017. (504.93 KB)
A. Demontis, Melis, M., Pintor, M., Jagielski, M., Biggio, B., Oprea, A., Nita-Rotaru, C., and Roli, F., Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks, in 28th Usenix Security Symposium, Santa Clara, California, USA, 2019, vol. 28th {USENIX} Security Symposium ({USENIX} Security 19), p. 321--338. (1.09 MB)
A. Demontis, Biggio, B., Fumera, G., and Roli, F., Super-Sparse Regression for Fast Age Estimation From Faces at Test Time, in 18th Int'l Conf. on Image Analysis and Processing (ICIAP), Genova, Italy, 2015, vol. Image Analysis and Processing (ICIAP 2015), pp. 551-562. (678.7 KB)
A. Demontis, Melis, M., Biggio, B., Fumera, G., and Roli, F., Super-sparse Learning in Similarity Spaces, IEEE Computational Intelligence Magazine, vol. 11, no. 4, pp. 36-45, 2016. (555.22 KB)
M. A. A. Dewan, Granger, E., Marcialis, G. L., Sabourin, R., and Roli, F., Adaptive Appearance Model Tracking for Still-to-Video Face Recognition, Pattern Recognition, vol. 49, no. 1, 2016. (6.51 MB)
M. A. A. Dewan, Granger, E., Sabourin, R., Roli, F., and Marcialis, G. L., A comparison of adaptive appearance methods for tracking faces in video surveillance, in 5th International Conference on Imaging for Crime Detection and Prevention (ICDP-13), 2013.
M. A. A. Dewan, Granger, E., Sabourin, R., Marcialis, G. L., and Roli, F., Video Face Recognition From A Single Still Image Using an Adaptive Appearance Model Tracker, in IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM 2015), 2015, pp. 192-202. (533.82 KB)
C. Di Ruberto and Putzu, L., Accurate blood cells segmentation through intuitionistic fuzzy set threshold, in Proceedings - 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014, 2015, pp. 57 – 64.
C. Di Ruberto, Loddo, A., and Putzu, L., Special Issue on Image Processing Techniques for Biomedical Applications, Applied Sciences (Switzerland), vol. 12, 2022.
C. Di Ruberto, Loddo, A., and Putzu, L., Histological image analysis by invariant descriptors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10484 LNCS, pp. 345 – 356, 2017.

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