Lightweight Generalized DeepFake Face Detection with WAVIE: Wavelet Augmented Vision Intermediate Embeddings

Authors: Arya Pulkit, Aditya Ruhela, Akarshan Kapoor, Arnav Bhavsar

Published: 2026-09-13 11:24:13+00:00

Comment: Accepted at the IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2026

AI Summary

WAVIE is an end-to-end deepfake detection architecture that addresses generalization limitations by combining spatial and frequency cues. It uses a frozen CLIP backbone, projects intermediate transformer embeddings, applies a Daubechies-6 discrete wavelet transform to refine low-frequency components, and reconstructs features for classification. Trained on FaceForensics++, WAVIE achieves superior cross-dataset performance on Celeb-DF-v1, Celeb-DF-v2, and WildDeepFake, outperforming several state-of-the-art baselines.

Abstract

Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability in real-world multimedia environments. This lack of generalization poses critical challenges for misinformation mitigation, digital forensics, and human-centric AI systems. Existing detectors perform well on the forgery methods they are trained on, but their accuracy drops sharply on unseen pipelines. To bridge this generalization gap, we propose WAVIE (Wavelet Augmented Vision Intermediate Embeddings), an end-to-end architecture that combines complementary spatial and frequency cues on top of a frozen CLIP backbone. WAVIE projects intermediate transformer embeddings through a lightweight learnable module, applies a three-level Daubechies-6 (db6) discrete wavelet transform (DWT), refines the low-frequency branch while preserving the high-frequency branch, reconstructs the feature via inverse DWT, and performs classification. Trained only on FaceForensics++, WAVIE achieves AUROC = 0.852 on Celeb-DF-v1, 0.852 on Celeb-DF-v2 and 0.831 on WildDeepFake (WDF) at the frame level, outperforming several state-of-the-art generalization baselines. Extensive ablation studies confirm the importance of both the wavelet module and the intermediate-feature aggregation for cross-dataset performance, highlighting the necessity of jointly leveraging spatial and frequency domains. These results position WAVIE as a strong baseline for deepfake detection in the wild.