Scaling Laws for Deepfake Detection
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The paper examines how implementation details—such as data preprocessing, augmentation, and optimization—affect deepfake detector performance more than core architectural choices. By systematically isolating these factors across training, inference, and incremental updates, the authors identify design choices that consistently improve accuracy and generalization. These findings provide architecture‑agnostic best practices that enable state‑of‑the‑art performance on the AI‑GenBench benchmark.
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