The Importance of Image Interpretation: Patterns of Semantic Misclassification in Real-World Adversarial Images

TL;DR

This paper advocates evaluating adversarial images based on semantic mismatch rather than label mismatch, providing a more realistic understanding of adversarial threats and enabling transferability assessment without prior label knowledge.

Contribution

It introduces a semantic-based evaluation framework for adversarial images, allowing analysis of transferability and semantic patterns in misclassifications in real-world classifiers.

Findings

Semantic mismatch evaluation reveals more realistic adversarial vulnerabilities.

Transfer attack demonstrates patterns in semantic misclassifications.

Semantic approach enables assessment without classifier label set during creation.

Abstract

Adversarial images are created with the intention of causing an image classifier to produce a misclassification. In this paper, we propose that adversarial images should be evaluated based on semantic mismatch, rather than label mismatch, as used in current work. In other words, we propose that an image of a "mug" would be considered adversarial if classified as "turnip", but not as "cup", as current systems would assume. Our novel idea of taking semantic misclassification into account in the evaluation of adversarial images offers two benefits. First, it is a more realistic conceptualization of what makes an image adversarial, which is important in order to fully understand the implications of adversarial images for security and privacy. Second, it makes it possible to evaluate the transferability of adversarial images to a real-world classifier, without requiring the classifier's label set to have been available during the creation of the images. The paper carries out an evaluation of a transfer attack on a real-world image classifier that is made possible by our semantic misclassification approach. The attack reveals patterns in the semantics of adversarial misclassifications that could not be investigated using conventional label mismatch.