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arXiv Machine Learning By Vaishnavi Sen, Cody Laurie, Rashida Hasan

Adaptive Gated Deepfake Detection for Low-Resolution and Resource-Constrained Environments

Read the original on arXiv Machine Learning →

The paper introduces AdaGate-DF, an adaptive gated deepfake detection framework designed for low-resolution and resource-constrained environments. By leveraging image-quality cues, the system routes samples through a dual multi-exit architecture, allowing high-quality images to exit earlier and reduce computational load. Evaluations on Celeb-DF and FaceForensics++ show that AdaGate-DF outperforms existing models such as MaD-CoRN and DefakeHop++ while maintaining low inference latency and robust performance across varying resolutions and class imbalance scenarios.

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