ViGuardQ: Quantized Video Deepfake Detection for Edge Devices

The growing use of edge devices, e.g. smartphones, for video conferencing and facial biometric authentication creates an increasing demand for efficient video deepfake detection. However, the substantial computational and memory requirements of existing detectors hinder their deployment on resource-constrained devices. Although quantization offers an effective means of model compression, conventional methods do not explicitly account for the subtle spatial artifacts and temporal inconsistencies essential to video deepfake detection, which can be distorted by low-precision representations. To address this challenge, we propose ViGuardQ, a post-training quantization framework tailored to video deepfake detection on edge devices. First, Dual-Evidence-Constrained (DEC) Rotation mitigates forgery-evidence relevant quantization distortion by identifying a sensitive channel subspace using within-frame and cross-frame differential decision gradients, and optimizing orthogonal rotations to preserve more forgery-evidence sensitive information. Then Evidence-guided Consistent Response (ECR) Calibration refines the quantizer's consistency in response to evidence under aligned and opposite perturbations. They together improve the preservation of and responses to subtle forgery evidence and greatly improve quantization accuracy. Extensive experiments on six benchmark datasets and eleven deepfake detectors demonstrate that ViGuardQ outperforms state-of-the-art quantization baselines. Specifically, we achieve 83.59% AUC scores at a lower 13.4% compression rate, compared with 68.07% achieved by the strongest baseline, PTQ4VM, at a 14.95% compression rate on the DFD dataset. Real-world deployment on edge devices further demonstrates our practical effectiveness. Our code will be released upon acceptance.