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

Author Affiliations +

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

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KEYWORDS

Convolution

Tunable filters

Machine learning

Databases

Feature extraction

Education and training

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