Our Favourite 5 publications - ICCV Tutorial on Adversarial Pattern Recognition
“If you know the enemy and know yourself, you need not fear the result of a hundred battles”
Sun Tzu, The art of war, 500 BC
Proposers' names, titles, affiliations, and emails:
• Battista Biggio, IAPR Member, IEEE Senior Member;
• Fabio Roli, IAPR Fellow, IEEE Fellow.
PRA Lab and Pluribus One, Italy
Email: battista.biggio (at) diee.unica.it
Email: surname_of_fabio (at) diee.unica.it
Our favourite 5 publications
- Biggio, B., Nelson, B., Laskov, P. Poisoning attacks against SVMs. In Langford, J. and Pineau, J. (eds.), 29th ICML, pp. 1807–1814. Omnipress, 2012.
- Biggio, B., Corona, I., Maiorca, D., Nelson, B., Srndic, N., Laskov, P., Giacinto, G., Roli, F. Evasion attacks against machine learning at test time. In ECML-PKDD, Part III, vol. 8190 of LNCS, pp. 387– 402. Springer Berlin Heidelberg, 2013.
- Biggio, B., Fumera, G., Roli, F. Security evaluation of pattern classifiers under attack. IEEE Trans. Knowl. Data Eng., 26 (4):984–996, 2014.
- Xiao, H., Biggio, B., Brown, G., Fumera, G., Eckert, C., Roli, F. Is feature selection secure against training data poisoning? In Bach, F. and Blei, D. (eds.), 32nd ICML, vol. 37, pp. 1689–1698, 2015.
- A. Demontis, M. Melis, B. Biggio, D. Maiorca, D. Arp, K. Rieck, I. Corona, G. Giacinto, F. Roli. Yes, Machine Learning can be More Secure! A case study on Android Malware Detection. IEEE Trans. Dependable and Secure Computing, 2017 (In press).
