Title: Detection of image recognition forgery technology under machine vision

Authors: Yong Liu; Yinjie Zhang; Zonghui Wang; Ruosi Cheng; Xu Zhao; Baolan Shi

Addresses: College of Cyberspace Security, PLA Strategic Support Force Information Engineering University, ZhengZhou 450001, Henan, China ' Research Institute of Intelligent Networks, Zhejiang Lab, Hangzhou, Zhejiang 311121, China ' College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China ' College of Cyberspace Security, PLA Strategic Support Force Information Engineering University, ZhengZhou 450001, Henan, China ' College of Cyberspace Security, PLA Strategic Support Force Information Engineering University, ZhengZhou 450001, Henan, China ' College of Engineering and Applied Science, University of Colorado Boulder, Boulder 80309, Colorado, USA

Abstract: Traditional image forgery detection methods have difficulty keeping up with the development of forgery technology and cannot effectively detect complex forged images. With the comprehensive use of modern computer vision and deep learning technology, this paper provides a new solution for the complex image forgery problem on the internet. By improving the Xception model, the accuracy of forged image detection can be improved, and the spread of forged images can be effectively identified and prevented. First, image forgery was carried out via forms such as the generative adversarial network (GAN), the cascaded refinement network (CRN), and implicit maximum likelihood estimation (IMLE). Second, this paper preprocessed the collected forged image dataset, extracted texture features, edge features, colour features and local discontinuities of the image and performed feature-level fusion of different types of features. Last, an improved Xception model was utilised to detect forged images. The experimental results showed that homologous data detection accuracy and single heterosource data detection accuracy were 97.8% and 86.9%, respectively, in the ProGAN dataset. The improved Xception model can effectively improve the accuracy of forged image detection and provide an effective detection method for complex forged images on the internet.

Keywords: image forgery; forgery detection; internet images; improved Xception model; machine vision; deep learning.

DOI: 10.1504/IJAHUC.2024.136852

International Journal of Ad Hoc and Ubiquitous Computing, 2024 Vol.45 No.2, pp.123 - 134

Received: 30 Oct 2023
Accepted: 15 Dec 2023
Published online: 22 Feb 2024 *

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