FDA: Feature Disruptive Attack

TL;DR

This paper introduces FDA, a new adversarial attack that disrupts deep neural network features at all layers, outperforming existing methods and affecting both classification and feature-based tasks without needing task-specific access.

Contribution

The paper proposes FDA, a novel feature-disruptive adversarial attack, along with new metrics OLNR and NLOR to evaluate attack damage, addressing limitations of existing methods.

Findings

FDA generates stronger adversaries than state-of-the-art methods.

FDA effectively disrupts feature representations across network layers.

FDA impacts feature-based tasks without network access.

Abstract

Though Deep Neural Networks (DNN) show excellent performance across various computer vision tasks, several works show their vulnerability to adversarial samples, i.e., image samples with imperceptible noise engineered to manipulate the network's prediction. Adversarial sample generation methods range from simple to complex optimization techniques. Majority of these methods generate adversaries through optimization objectives that are tied to the pre-softmax or softmax output of the network. In this work we, (i) show the drawbacks of such attacks, (ii) propose two new evaluation metrics: Old Label New Rank (OLNR) and New Label Old Rank (NLOR) in order to quantify the extent of damage made by an attack, and (iii) propose a new adversarial attack FDA: Feature Disruptive Attack, to address the drawbacks of existing attacks. FDA works by generating image perturbation that disrupt features at each layer of the network and causes deep-features to be highly corrupt. This allows FDA adversaries to severely reduce the performance of deep networks. We experimentally validate that FDA generates stronger adversaries than other state-of-the-art methods for image classification, even in the presence of various defense measures. More importantly, we show that FDA disrupts feature-representation based tasks even without access to the task-specific network or methodology. Code available at: https://github.com/BardOfCodes/fda