Mitigating the Impact of Adversarial Attacks in Very Deep Networks

TL;DR

This paper introduces a novel defense mechanism combining a Defensive Feature Layer and Polarized Contrastive Loss to improve the robustness of deep neural networks against data poisoning attacks, demonstrating superior performance on CIFAR-10 and MNIST datasets.

Contribution

It proposes an attack-agnostic defense method integrating a Defensive Feature Layer and Polarized Contrastive Loss to mitigate data poisoning effects in deep networks.

Findings

Enhanced robustness against data poisoning attacks

Improved classification accuracy on attacked datasets

Outperforms recent peer techniques

Abstract

Deep Neural Network (DNN) models have vulnerabilities related to security concerns, with attackers usually employing complex hacking techniques to expose their structures. Data poisoning-enabled perturbation attacks are complex adversarial ones that inject false data into models. They negatively impact the learning process, with no benefit to deeper networks, as they degrade a model's accuracy and convergence rates. In this paper, we propose an attack-agnostic-based defense method for mitigating their influence. In it, a Defensive Feature Layer (DFL) is integrated with a well-known DNN architecture which assists in neutralizing the effects of illegitimate perturbation samples in the feature space. To boost the robustness and trustworthiness of this method for correctly classifying attacked input samples, we regularize the hidden space of a trained model with a discriminative loss function called Polarized Contrastive Loss (PCL). It improves discrimination among samples in different classes and maintains the resemblance of those in the same class. Also, we integrate a DFL and PCL in a compact model for defending against data poisoning attacks. This method is trained and tested using the CIFAR-10 and MNIST datasets with data poisoning-enabled perturbation attacks, with the experimental results revealing its excellent performance compared with those of recent peer techniques.