An Ultra-Compact Wavelet-Enhanced Hybrid Framework for Real-Time Edge Deepfake Detection: Experimental Validation on an NVIDIA Jetson Orin Nano
Year: 2026
Type: article
Abstract: 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.
Source: IoT
DOI: 10.3390/iot7040083
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,448
OpenAlex ID: W7214065592