An adversarial attack approach for eXplainable AI evaluation on deepfake detection models

TL;DR

This paper evaluates the effectiveness of XAI tools on deepfake detection models, demonstrating that generic evaluation methods are inadequate and proposing a specialized approach for better assessment.

Contribution

It introduces a novel XAI evaluation method tailored specifically for deepfake detection models, addressing limitations of generic evaluation techniques.

Findings

Generic XAI evaluation methods are less effective for deepfake models

Proposed approach provides more meaningful evaluation results

Experimental results validate the new evaluation method

Abstract

With the rising concern on model interpretability, the application of eXplainable AI (XAI) tools on deepfake detection models has been a topic of interest recently. In image classification tasks, XAI tools highlight pixels influencing the decision given by a model. This helps in troubleshooting the model and determining areas that may require further tuning of parameters. With a wide range of tools available in the market, choosing the right tool for a model becomes necessary as each one may highlight different sets of pixels for a given image. There is a need to evaluate different tools and decide the best performing ones among them. Generic XAI evaluation methods like insertion or removal of salient pixels/segments are applicable for general image classification tasks but may produce less meaningful results when applied on deepfake detection models due to their functionality. In this paper, we perform experiments to show that generic removal/insertion XAI evaluation methods are not suitable for deepfake detection models. We also propose and implement an XAI evaluation approach specifically suited for deepfake detection models.