Data-Dependent Randomized Smoothing
This work provides a significant improvement in certified robustness for deep neural networks, which is crucial for safety-critical AI applications.
This paper proposes a method to optimize the variance of Gaussian randomized smoothing for each input, leading to a memory-enhanced data-dependent smooth classifier. This approach improves certified accuracy by 9% on CIFAR10 and 6% on ImageNet for a radius of 0.5 compared to the strongest baseline.
Randomized smoothing is a recent technique that achieves state-of-art performance in training certifiably robust deep neural networks. While the smoothing family of distributions is often connected to the choice of the norm used for certification, the parameters of these distributions are always set as global hyper parameters independent from the input data on which a network is certified. In this work, we revisit Gaussian randomized smoothing and show that the variance of the Gaussian distribution can be optimized at each input so as to maximize the certification radius for the construction of the smooth classifier. Since the data dependent classifier does not directly enjoy sound certification with existing approaches, we propose a memory-enhanced data dependent smooth classifier that is certifiable by construction. This new approach is generic, parameter-free, and easy to implement. In fact, we show that our data dependent framework can be seamlessly incorporated into 3 randomized smoothing approaches, leading to consistent improved certified accuracy. When this framework is used in the training routine of these approaches followed by a data dependent certification, we achieve 9% and 6% improvement over the certified accuracy of the strongest baseline for a radius of 0.5 on CIFAR10 and ImageNet.