Publications
, «Adversarial Feature Selection Against Evasion Attacks», IEEE Transactions on Cybernetics, vol 46, n° 3, pagg 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, pag -, 2016.
, «Secure Kernel Machines against Evasion Attacks», in 9th ACM Workshop on Artificial Intelligence and Security, Vienna, Austria, 2016, pagg 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, pagg 322-332. (425.68 KB)
(425.68 KB), «Super-sparse Learning in Similarity Spaces», IEEE Computational Intelligence Magazine, vol 11, n° 4, pagg 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), pagg 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, pagg 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, pagg 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, pagg 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, n° 11, pagg 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, n° 3, pagg 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, pagg 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, pagg 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, pagg 524-528. (431.78 KB)
(431.78 KB), «Multi-Stage Ranking Approach for Fast Person Re-Identification», IET Computer Vision, vol 12, n° 4, pag 7, 2018. (1.07 MB)
(1.07 MB), «Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning», Pattern Recognition, vol 84, pagg 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, pag 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, pagg 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), pag 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, n° 4, pagg 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.
