Publications
, “Adversarial Feature Selection Against Evasion Attacks”, IEEE Transactions on Cybernetics, vol. 46, no. 3, pp. 766-777, 2016.
(2.12 MB)
(2.12 MB), “Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015”, Image and Vision Computing, p. -, 2016.
, “Secure Kernel Machines against Evasion Attacks”, in 9th ACM Workshop on Artificial Intelligence and Security, Vienna, Austria, 2016, pp. 59-69. (686.41 KB)
(686.41 KB), “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)
(425.68 KB), “Super-sparse Learning in Similarity Spaces”, IEEE Computational Intelligence Magazine, vol. 11, no. 4, pp. 36-45, 2016. (555.22 KB)
(555.22 KB), “Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid”, in ICCV 2017 Workshop on Vision in Practice on Autonomous Robots (ViPAR), Venice, Italy, 2017, vol. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 751-759. (3.16 MB)
(3.16 MB), “Deepsquatting: Learning-based Typosquatting Detection at Deeper Domain Levels”, in 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017), 2017, vol. 10640 of LNCS, pp. 347-358. (1.21 MB)
(1.21 MB), “DeltaPhish: Detecting Phishing Webpages in Compromised Websites”, 22nd European Symposium on Research in Computer Security (ESORICS), vol. 10492. Springer International Publishing, Norway, September 11-15, 2017, pp. 370–388, 2017. (4.13 MB)
(4.13 MB), “Designing multi-label classifiers that maximize F measures: state of the art”, Pattern Recognition, vol. 61, 2017. (452.28 KB)
(452.28 KB), “A Parameter Randomization Approach for Constructing Classifier Ensembles”, Pattern Recognition, vol. 69, pp. 1-13, 2017. (448.73 KB)
(448.73 KB), “Randomized Prediction Games for Adversarial Machine Learning”, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 11, pp. 2466-2478, 2017. (1.52 MB) (256.21 KB)
(1.52 MB) (256.21 KB), “Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 3, pp. 561-575, 2017. (5.7 MB)
(5.7 MB), “Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization”, in 10th ACM Workshop on Artificial Intelligence and Security, 2017, pp. 27-38. (4.08 MB)
(4.08 MB), “Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables”, in 2018 26th European Signal Processing Conference (EUSIPCO), Rome, 2018, pp. 533-537. (674.62 KB)
(674.62 KB), “Explaining Black-box Android Malware Detection”, in 26th European Signal Processing Conference (EUSIPCO '18), Rome, Italy, 2018, pp. 524-528. (431.78 KB)
(431.78 KB), “Multi-Stage Ranking Approach for Fast Person Re-Identification”, IET Computer Vision, vol. 12, no. 4, p. 7, 2018. (1.07 MB)
(1.07 MB), “Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning”, Pattern Recognition, vol. 84, pp. 317-331, 2018. (3.76 MB)
(3.76 MB), “DEICTIC: a Compositional and Declarative Gesture Description based on Hidden Markov Model”, International Journal of Human-Computer Studies, vol. 122, p. 20, 2019. (1.69 MB)
(1.69 MB), “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)
(801.85 KB), “Towards Quality Assurance of Software Product Lines with Adversarial Configurations”, in Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A, New York, NY, USA, 2019, pp. 277–288. (2.09 MB)
(2.09 MB), “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)
(1.09 MB), “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)
(3.61 MB), “Adversarial Detection of Flash Malware: Limitations and Open Issues”, Computers & Security, vol. 96, 2020. (1.08 MB)
(1.08 MB), “Deep Neural Rejection against Adversarial Examples”, EURASIP Journal on Information Security, vol. 5, 2020.
, “Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection”, ACM Trans. Priv. Secur., vol. 24, 2021.
