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

Paper recorded by Signals 4 on 2026-09-04 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-04 on arXiv · recorded by Signals 4 on 2026-09-07

Category: cs.LG · 机器学习 · first seen 2026-09-07

Abstract

Deepfake detection models often rely on high-quality inputs, fixed inference paths, and computationally expensive architectures, limiting their use in low-resolution and resource-constrained settings. This paper proposes AdaGate-DF, an adaptive gated deepfake detection framework that uses image-quality cues to route samples through a dual multi-exit system so high-quality images can exit earlier a

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