Open Access

ARTICLE

Face Forgery Detection via Multi-Scale Dual-Modality Mutual Enhancement Network

Yuanqing Ding^1,2, Hanming Zhai¹, Qiming Ma¹, Liang Zhang¹, Lei Shao², Fanliang Bu^1,*

1 School of Information Network Security, People’s Public Security University of China, Beijing, 100038, China
2 Department of Criminal Investigation, Sichuan Police College, Luzhou, 646000, China

* Corresponding Author: Fanliang Bu. Email:

(This article belongs to the Special Issue: Challenges and Innovations in Multimedia Encryption and Information Security)

Computers, Materials & Continua 2025, 85(1), 905-923. https://doi.org/10.32604/cmc.2025.066307

Received 04 April 2025; Accepted 12 June 2025; Issue published 29 August 2025

Abstract

As the use of deepfake facial videos proliferate, the associated threats to social security and integrity cannot be overstated. Effective methods for detecting forged facial videos are thus urgently needed. While many deep learning-based facial forgery detection approaches show promise, they often fail to delve deeply into the complex relationships between image features and forgery indicators, limiting their effectiveness to specific forgery techniques. To address this challenge, we propose a dual-branch collaborative deepfake detection network. The network processes video frame images as input, where a specialized noise extraction module initially extracts the noise feature maps. Subsequently, the original facial images and corresponding noise maps are directed into two parallel feature extraction branches to concurrently learn texture and noise forgery clues. An attention mechanism is employed between the two branches to facilitate mutual guidance and enhancement of texture and noise features across four different scales. This dual-modal feature integration enhances sensitivity to forgery artifacts and boosts generalization ability across various forgery techniques. Features from both branches are then effectively combined and processed through a multi-layer perception layer to distinguish between real and forged video. Experimental results on benchmark deepfake detection datasets demonstrate that our approach outperforms existing state-of-the-art methods in terms of detection performance, accuracy, and generalization ability.

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Keywords

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