An adversarial attack approach for eXplainable AI evaluation on deepfake detection models
This addresses the need for reliable XAI evaluation in deepfake detection, which is crucial for model interpretability and security, but it is incremental as it adapts existing concepts to a specific application.
The paper tackled the problem of evaluating eXplainable AI (XAI) tools for deepfake detection models, showing that generic evaluation methods are unsuitable and proposing a new approach specifically designed for this domain.
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.