Certifiably Robust Interpretation via Renyi Differential Privacy

Abstract

Motivated by the recent discovery that the interpretation maps of CNNs could easily be manipulated by adversarial attacks against network interpretability, we study the problem of interpretation robustness from a new perspective of differential privacy (RDP). The advantages of our Renyi-Robust-Smooth (RDP-based interpretation method) are three-folds. First, it can offer provable and certifiable top-k robustness. That is, the top-k important attributions of the interpretation map are provably robust under any input perturbation with bounded _d-norm (for any d 1, including d = ). Second, our proposed method offers 10\% better experimental robustness than existing approaches in terms of the top-k attributions. Remarkably, the accuracy of Renyi-Robust-Smooth also outperforms existing approaches. Third, our method can provide a smooth tradeoff between robustness and computational efficiency. Experimentally, its top-k attributions are twice more robust than existing approaches when the computational resources are highly constrained.

Tasks

Reproductions