REGLO: Provable Neural Network Repair for Global Robustness Properties

Abstract

We present REGLO, a novel methodology for repairing neural networks to satisfy global robustness properties. In contrast to existing works that focus on local robustness, i.e., robustness of individual inputs, REGLO tackles global robustness, a strictly stronger notion that requires robustness for all inputs within a region. Leveraging an observation that any counterexample to a global robustness property must exhibit a corresponding large gradient, REGLO first identifies violating regions where the counterexamples reside, then uses verified robustness bounds on these regions to formulate a robust optimization problem to compute a minimal weight change in the network that will provably repair the violations. Experimental results demonstrate the effectiveness of REGLO across a set of benchmarks.