Adversarial Sample Detection Through Neural Network Transport Dynamics

Autor:	Karkar, Skander, Gallinari, Patrick, Rakotomamonjy, Alain
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We propose a detector of adversarial samples that is based on the view of neural networks as discrete dynamic systems. The detector tells clean inputs from abnormal ones by comparing the discrete vector fields they follow through the layers. We also show that regularizing this vector field during training makes the network more regular on the data distribution's support, thus making the activations of clean inputs more distinguishable from those of abnormal ones. Experimentally, we compare our detector favorably to other detectors on seen and unseen attacks, and show that the regularization of the network's dynamics improves the performance of adversarial detectors that use the internal embeddings as inputs, while also improving test accuracy. Comment: ECML PKDD 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.04252 Zobrazit plný text záznamu View this record from Arxiv