Stochastic coordinate descent for 01 loss and its sensitivity to adversarial attacks

TY - GEN

T1 - Stochastic coordinate descent for 01 loss and its sensitivity to adversarial attacks

AU - Xie, Meiyan

AU - Xue, Yunzhe

AU - Roshan, Usman

N1 - Publisher Copyright: © 2019 IEEE.

PY - 2019/12

Y1 - 2019/12

N2 - The 01 loss while hard to optimize is least sensitive to outliers compared to its continuous differentiable counterparts, namely hinge and logistic loss. Recently the 01 loss has been shown to be most robust compared to surrogate losses against corrupted labels which can be interpreted as adversarial attacks. Here we propose a stochastic coordinate descent heuristic for linear 01 loss classification. We implement and study our heuristic on real datasets from the UCI machine learning archive and find our method to be comparable to the support vector machine in accuracy and tractable in training time. We conjecture that the 01 loss may be harder to attack in a black box setting due to its non-continuity and infinite solution space. We train our linear classifier in a one-vs-one multi-class strategy on CIFAR10 and STL10 image benchmark datasets. In both cases we find our classifier to have the same accuracy as the linear support vector machine but more resilient to black box attacks. On CIFAR10 the linear support vector machine has 0% on adversarial examples while the 01 loss classifier hovers about 10%. On STL10 the linear support vector machine has 0% accuracy whereas 01 loss is at 10%. Our work here suggests that 01 loss may be more resilient to adversarial attacks than the hinge loss and further work is required.

AB - The 01 loss while hard to optimize is least sensitive to outliers compared to its continuous differentiable counterparts, namely hinge and logistic loss. Recently the 01 loss has been shown to be most robust compared to surrogate losses against corrupted labels which can be interpreted as adversarial attacks. Here we propose a stochastic coordinate descent heuristic for linear 01 loss classification. We implement and study our heuristic on real datasets from the UCI machine learning archive and find our method to be comparable to the support vector machine in accuracy and tractable in training time. We conjecture that the 01 loss may be harder to attack in a black box setting due to its non-continuity and infinite solution space. We train our linear classifier in a one-vs-one multi-class strategy on CIFAR10 and STL10 image benchmark datasets. In both cases we find our classifier to have the same accuracy as the linear support vector machine but more resilient to black box attacks. On CIFAR10 the linear support vector machine has 0% on adversarial examples while the 01 loss classifier hovers about 10%. On STL10 the linear support vector machine has 0% accuracy whereas 01 loss is at 10%. Our work here suggests that 01 loss may be more resilient to adversarial attacks than the hinge loss and further work is required.

KW - 01 loss

KW - Adversarial attacks

KW - Stochastic coordinate descent

UR - http://www.scopus.com/inward/record.url?scp=85080954777&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85080954777&partnerID=8YFLogxK

U2 - 10.1109/ICMLA.2019.00056

DO - 10.1109/ICMLA.2019.00056

M3 - Conference contribution

T3 - Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

SP - 299

EP - 304

BT - Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

A2 - Wani, M. Arif

A2 - Khoshgoftaar, Taghi M.

A2 - Wang, Dingding

A2 - Wang, Huanjing

A2 - Seliya, Naeem

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

Y2 - 16 December 2019 through 19 December 2019

ER -