Deep Probabilistic Models to Detect Data Poisoning Attacks

TL;DR

This paper explores methods to detect data poisoning in deep neural networks by analyzing uncertainty estimates derived from deep features and Bayesian inference, effectively identifying malicious training samples.

Contribution

It introduces two novel approaches for detecting backdoor data poisoning attacks using uncertainty quantification in deep neural networks.

Findings

Uncertainty estimates can effectively distinguish poisoned from clean samples.

Modeling deep features with probability distributions aids in poisoning detection.

Bayesian neural networks provide reliable uncertainty measures for identifying malicious data.

Abstract

Data poisoning attacks compromise the integrity of machine-learning models by introducing malicious training samples to influence the results during test time. In this work, we investigate backdoor data poisoning attack on deep neural networks (DNNs) by inserting a backdoor pattern in the training images. The resulting attack will misclassify poisoned test samples while maintaining high accuracies for the clean test-set. We present two approaches for detection of such poisoned samples by quantifying the uncertainty estimates associated with the trained models. In the first approach, we model the outputs of the various layers (deep features) with parametric probability distributions learnt from the clean held-out dataset. At inference, the likelihoods of deep features w.r.t these distributions are calculated to derive uncertainty estimates. In the second approach, we use Bayesian deep neural networks trained with mean-field variational inference to estimate model uncertainty associated with the predictions. The uncertainty estimates from these methods are used to discriminate clean from the poisoned samples.