Neural networks for HEP tasks can be fooled at significant rates by subtle perturbations inside uncertainty envelopes, revealing hidden systematics not captured by conventional methods.
Moosavi-Dezfooli, A
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
State-of-the-art deep neural networks have achieved impressive results on many image classification tasks. However, these same architectures have been shown to be unstable to small, well sought, perturbations of the images. Despite the importance of this phenomenon, no effective methods have been proposed to accurately compute the robustness of state-of-the-art deep classifiers to such perturbations on large-scale datasets. In this paper, we fill this gap and propose the DeepFool algorithm to efficiently compute perturbations that fool deep networks, and thus reliably quantify the robustness of these classifiers. Extensive experimental results show that our approach outperforms recent methods in the task of computing adversarial perturbations and making classifiers more robust.
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
ADIS-GAN disentangles affine transformations in a GAN to achieve over 98% classification accuracy on MNIST within 30 degrees rotation and over 90% under FGSM and PGD attacks while generating rotation and scaling factors.
citing papers explorer
-
Uncovering Hidden Systematics in Neural Network Models for High Energy Physics
Neural networks for HEP tasks can be fooled at significant rates by subtle perturbations inside uncertainty envelopes, revealing hidden systematics not captured by conventional methods.
-
Affine Disentangled GAN for Interpretable and Robust AV Perception
ADIS-GAN disentangles affine transformations in a GAN to achieve over 98% classification accuracy on MNIST within 30 degrees rotation and over 90% under FGSM and PGD attacks while generating rotation and scaling factors.