Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis

TL;DR

This paper introduces TaintRadar, a generic detection system for localized adversarial examples in deep neural networks, analyzing critical regions to identify manipulations without retraining, effective against sophisticated attacks in digital and physical settings.

Contribution

Proposes TaintRadar, a novel, training-free method for detecting localized adversarial examples by analyzing changes in label rankings after removing critical regions.

Findings

TaintRadar effectively detects localized adversarial attacks in digital images.

The method is robust against physical-world adversarial examples.

TaintRadar outperforms existing defenses without requiring model retraining.

Abstract

Deep neural networks (DNNs) have been applied in a wide range of applications,e.g.,face recognition and image classification; however,they are vulnerable to adversarial examples. By adding a small amount of imperceptible perturbations,an attacker can easily manipulate the outputs of a DNN. Particularly,the localized adversarial examples only perturb a small and contiguous region of the target object,so that they are robust and effective in both digital and physical worlds. Although the localized adversarial examples have more severe real-world impacts than traditional pixel attacks,they have not been well addressed in the literature. In this paper,we propose a generic defense system called TaintRadar to accurately detect localized adversarial examples via analyzing critical regions that have been manipulated by attackers. The main idea is that when removing critical regions from input images,the ranking changes of adversarial labels will be larger than those of benign labels. Compared with existing defense solutions,TaintRadar can effectively capture sophisticated localized partial attacks, e.g.,the eye-glasses attack,while not requiring additional training or fine-tuning of the original model's structure. Comprehensive experiments have been conducted in both digital and physical worlds to verify the effectiveness and robustness of our defense.