Generalized Design Choices for Deepfake Detectors

• URL: http://arxiv.org/abs/2511.21507v1
• Date: Wed, 26 Nov 2025 15:40:58 GMT
• Title: Generalized Design Choices for Deepfake Detectors
• Abstract summary: We investigate how different design choices influence the accuracy and generalization capabilities of deepfake detection models.<n>By isolating the impact of individual factors, we aim to establish robust, architecture-agnostic best practices for the design and development of future deepfake detection systems.<n>Our experiments identify a set of design choices that consistently improve deepfake detection and enable state-of-the-art performance on the AI-GenBench benchmark.
• Score: 2.403371623345458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The effectiveness of deepfake detection methods often depends less on their core design and more on implementation details such as data preprocessing, augmentation strategies, and optimization techniques. These factors make it difficult to fairly compare detectors and to understand which factors truly contribute to their performance. To address this, we systematically investigate how different design choices influence the accuracy and generalization capabilities of deepfake detection models, focusing on aspects related to training, inference, and incremental updates. By isolating the impact of individual factors, we aim to establish robust, architecture-agnostic best practices for the design and development of future deepfake detection systems. Our experiments identify a set of design choices that consistently improve deepfake detection and enable state-of-the-art performance on the AI-GenBench benchmark.

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