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

  1. Biggio, B., Nelson, B., Laskov, P. Poisoning attacks against SVMs. In Langford, J. and Pineau, J. (eds.), 29th ICML, pp. 1807–1814. Omnipress, 2012.
  2. 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.
  3. Biggio, B., Fumera, G., Roli, F. Security evaluation of pattern classifiers under attack. IEEE Trans. Knowl. Data Eng., 26 (4):984–996, 2014.
  4. 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.
  5. 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).