DeformRS: Certifying Input Deformations with Randomized Smoothing
نویسندگان
چکیده
Deep neural networks are vulnerable to input deformations in the form of vector fields pixel displacements and other parameterized geometric e.g. translations, rotations, etc. Current deformation certification methods either (i) do not scale deep on large datasets, or (ii) can only certify a specific class deformations, rotations. We reformulate randomized smoothing setting for both general field propose DeformRS-VF DeformRS-Par, respectively. Our new formulation scales datasets. For instance, DeformRS-Par certifies rich covering scaling, affine visually aligned such as ones by Discrete-Cosine-Transform basis. Extensive experiments MNIST, CIFAR10, ImageNet show competitive performance achieving certified accuracy 39\% against perturbed rotations set [-10 degree, 10 degree] ImageNet.
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ژورنال
عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence
سال: 2022
ISSN: ['2159-5399', '2374-3468']
DOI: https://doi.org/10.1609/aaai.v36i6.20546