An Ultra-Compact Wavelet-Enhanced Hybrid Framework for Real-Time Edge Deepfake Detection: Experimental Validation on an NVIDIA Jetson Orin Nano

Deepfake manipulation has become increasingly difficult to detect as generative models continue to produce highly realistic facial content. This work presents the Frequency-Aware Lightweight Hybrid Network (FALH-Net Lite), a compact deepfake classification framework that combines spatial- and frequency-domain representations for resource-constrained edge deployment. The proposed framework integrates grayscale intensity with three high-frequency Discrete Wavelet Transform (DWT) sub-bands (LH, HL, and HH) to form a four-channel input, which is processed using an adapted YOLO11n-cls backbone. A Frequency-Aware Squeeze-and-Excitation (FreqSE) module is incorporated to recalibrate channel responses, while Focal Loss is employed during training to address class imbalance. The Celeb-DF-v2 dataset is partitioned at the video level before frame extraction to reduce the risk of near-duplicate leakage between training and validation data. Under the corrected video-level evaluation, FALH-Net Lite achieves an accuracy of 87.7%, with a balanced accuracy of 62.1%, an ROC-AUC of 73.6%, an F1-score of 93.2%, and an MCC of 36.7%. The model contains approximately 1.26 million parameters and requires 0.4885 GFLOPs, providing a compact computational profile for edge inference. Deployment experiments on the NVIDIA Jetson Orin Nano and Raspberry Pi 5 further evaluate the feasibility of executing the proposed classification pipeline on resource-constrained hardware. Cross-dataset validation and broader multi-run statistical evaluation remain directions for future work.