Metamorphic Detection of Adversarial Examples in Deep Learning Models With Affine Transformations

TL;DR

This paper introduces a metamorphic testing approach using affine transformations to automatically detect imperceptible adversarial examples in deep learning image classifiers, enhancing security in AI applications.

Contribution

It proposes a novel metamorphic testing method based on affine transformations to identify adversarial images with high accuracy, addressing a critical security challenge.

Findings

High detection accuracy for adversarial examples

Effective in identifying imperceptible manipulations

Applicable to safety-critical AI systems

Abstract

Adversarial attacks are small, carefully crafted perturbations, imperceptible to the naked eye; that when added to an image cause deep learning models to misclassify the image with potentially detrimental outcomes. With the rise of artificial intelligence models in consumer safety and security intensive industries such as self-driving cars, camera surveillance and face recognition, there is a growing need for guarding against adversarial attacks. In this paper, we present an approach that uses metamorphic testing principles to automatically detect such adversarial attacks. The approach can detect image manipulations that are so small, that they are impossible to detect by a human through visual inspection. By applying metamorphic relations based on distance ratio preserving affine image transformations which compare the behavior of the original and transformed image; we show that our proposed approach can determine whether or not the input image is adversarial with a high degree of accuracy.