Oral
Theoretically Principled Trade-off between Robustness and Accuracy
Hongyang Zhang · Yaodong Yu · Jiantao Jiao · Eric Xing · Laurent El Ghaoui · Michael Jordan

Wed Jun 12th 11:00 -- 11:20 AM @ Grand Ballroom

We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. Although the problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. In this work, we quantify the trade-off in terms of the gap between the risk for adversarial examples and the risk for non-adversarial examples. The challenge is to provide tight bounds on this quantity in terms of a surrogate loss. We give an optimal upper bound on this quantity in terms of classification-calibrated loss, which matches the lower bound in the worst case. Inspired by our theoretical analysis, we also design a new defense method, TRADES, to trade adversarial robustness off against accuracy. Our proposed algorithm performs well experimentally in real-world datasets. The methodology is the foundation of our entry to the adversarial competition of a 2018 conference in which we won the 1st place out of ~2,000 submissions, surpassing the runner-up approach by 11.41% in terms of mean L_2 perturbation distance.

Author Information

Hongyang Zhang (CMU & TTIC)
Yaodong Yu (University of Virginia)
Jiantao Jiao (University of California, Berkeley)
Eric Xing (Petuum Inc. and CMU)
Laurent El Ghaoui (UC Berkeley)
Michael Jordan (UC Berkeley)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors