XAI-Based Detection of Adversarial Attacks on Deepfake Detectors

TL;DR

This paper presents an XAI-based methodology for detecting adversarial attacks on deepfake detectors, improving interpretability and identifying vulnerabilities without compromising detection performance.

Contribution

It introduces a novel XAI-driven approach that visualizes decision factors and uses feature embeddings for robust adversarial attack detection in deepfake systems.

Findings

Effective visualization of decision-making factors

Detection of adversarial attacks without performance loss

Potential for future deepfake detection improvements

Abstract

We introduce a novel methodology for identifying adversarial attacks on deepfake detectors using eXplainable Artificial Intelligence (XAI). In an era characterized by digital advancement, deepfakes have emerged as a potent tool, creating a demand for efficient detection systems. However, these systems are frequently targeted by adversarial attacks that inhibit their performance. We address this gap, developing a defensible deepfake detector by leveraging the power of XAI. The proposed methodology uses XAI to generate interpretability maps for a given method, providing explicit visualizations of decision-making factors within the AI models. We subsequently employ a pretrained feature extractor that processes both the input image and its corresponding XAI image. The feature embeddings extracted from this process are then used for training a simple yet effective classifier. Our approach contributes not only to the detection of deepfakes but also enhances the understanding of possible adversarial attacks, pinpointing potential vulnerabilities. Furthermore, this approach does not change the performance of the deepfake detector. The paper demonstrates promising results suggesting a potential pathway for future deepfake detection mechanisms. We believe this study will serve as a valuable contribution to the community, sparking much-needed discourse on safeguarding deepfake detectors.