Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2025.063141
Journal Menu
Special Issues
Table of Content

All issues

Online First

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

SMNDNet for Multiple Types of Deepfake Image Detection

Qin Wang1, Xiaofeng Wang2,*, Jianghua Li2, Ruidong Han2, Zinian Liu1, Mingtao Guo3
1 Department of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China
2 Department of Mathematics, Xi’an University of Technology, Xi’an, 710054, China
3 The National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, 610065, China
* Corresponding Author: Xiaofeng Wang. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.063141

Received 06 January 2025; Accepted 27 February 2025; Published online 26 March 2025

Abstract

The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images, which limits their ability to keep pace with the rapid advancements in deepfake technology. Therefore, in this study, we propose a novel algorithm, Stereo Mixture Density Network (SMNDNet), which can detect multiple types of deepfake face manipulations using a single network framework. SMNDNet is an end-to-end CNN-based network specially designed for detecting various manipulation types of deepfake face images. First, we design a Subtle Distinguishable Feature Enhancement Module to emphasize the differentiation between authentic and forged features. Second, we introduce a Multi-Scale Forged Region Adaptive Module that dynamically adapts to extract forged features from images of varying synthesis scales. Third, we integrate a Nonlinear Expression Capability Enhancement Module to augment the model’s capacity for capturing intricate nonlinear patterns across various types of deepfakes. Collectively, these modules empower our model to efficiently extract forgery features from diverse manipulation types, ensuring a more satisfactory performance in multiple-types deepfake detection. Experiments show that the proposed method outperforms alternative approaches in detection accuracy and AUC across all four types of deepfake images. It also demonstrates strong generalization on cross-dataset and cross-type detection, along with robust performance against post-processing manipulations.

Keywords

Convolutional neural network; deepfake detection; generative adversarial network; feature enhancement

  • 72

    View

  • 14

    Download

  • 0

    Like

Related articles

Share Link