DeformRS: Certifying Input Deformations with Randomized Smoothing
This addresses the problem of certifying robustness against geometric deformations for deep learning models, offering a scalable solution that covers multiple deformation types, though it is incremental in improving upon existing certification methods.
The paper tackled the vulnerability of deep neural networks to input deformations like rotations and translations by proposing DeformRS, a method that certifies such deformations using randomized smoothing, achieving a certified accuracy of 39% against perturbed rotations on ImageNet.
Deep neural networks are vulnerable to input deformations in the form of vector fields of pixel displacements and to other parameterized geometric deformations e.g. translations, rotations, etc. Current input deformation certification methods either 1. do not scale to deep networks on large input datasets, or 2. can only certify a specific class of deformations, e.g. only rotations. We reformulate certification in randomized smoothing setting for both general vector field and parameterized deformations and propose DeformRS-VF and DeformRS-Par, respectively. Our new formulation scales to large networks on large input datasets. For instance, DeformRS-Par certifies rich deformations, covering translations, rotations, scaling, affine deformations, and other visually aligned deformations such as ones parameterized by Discrete-Cosine-Transform basis. Extensive experiments on MNIST, CIFAR10, and ImageNet show competitive performance of DeformRS-Par achieving a certified accuracy of $39\%$ against perturbed rotations in the set $[-10\degree,10\degree]$ on ImageNet.