Computer-generated image detection based on deep LBP network

Ying Zhang; Nan Zhu; Xu Zhang; Kun Wang

doi:10.1117/12.3026265

8 April 2024 Computer-generated image detection based on deep LBP network

Ying Zhang, Nan Zhu, Xu Zhang, Kun Wang

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130903M (2024) https://doi.org/10.1117/12.3026265
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

The rapid advancement of computer image production technology presents a grave peril to the trustworthiness of digital images, necessitating a great practical requirement for research on computer generated image detection technology in the realms of digital forensics and judicial assessment. Therefore, we propose a deep local binary pattern network (DLBPNet) to detect computer-generated image (CGI). Specifically, we first designed a deep local binary pattern module, which has three parallel branches, each of which utilizes a 1 × 1 convolution layer to learn the correlation between color channels, a learnable pre-processing filter to eliminate information redundancy, and an LBP submodule to extract low-level discriminative features. Then we feed the output of this module into successive generalized central difference convolution modules to further learn the higher-level hierarchical representation for making decision. Our proposed DLBP-Net network was confirmed to be effective in both detection accuracy and generalization ability through extensive experiments, which yielded detection accuracy of 94.35% on SPL2018 dataset, 94.03% on DSToK dataset, and 93.87% on the mixed dataset.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ying Zhang, Nan Zhu, Xu Zhang, and Kun Wang "Computer-generated image detection based on deep LBP network", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130903M (8 April 2024); https://doi.org/10.1117/12.3026265

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Convolution

Tunable filters

Machine learning

Databases

Feature extraction

Education and training

Optical filters

Show All Keywords

Keywords/Phrases

Search In:

Publication Years