XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
Ben Pinhasov Raz Lapid Rony Ohayon Moshe Sipper Yehudit Aperstein
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.