GAN-generated Fake Images Recognition Based on Improved ConvNeXt

doi:10.3969/j.issn.1006-2475.2024.04.007

Abstract

Abstract:
Abstract: In order to distinguish the authenticity of face images in social networks， a recognition method based on ConvNeXt for face image generated by Generative adversarial networks （GAN） is proposed. The ConvNeXt network structure is used as the main body， using the color features and spatial texture features of the face image， and multi-channel combination input （Multichannel Input， MCI） with multi-color space is used to expand the learning range of the network， while channel attention mechanism and spatial attention mechanism are introduced to highlight the differences between real and fake face images in color components and spatial features， and then the detection and recognition of fake face images are achieved. The experimental results show that the recognition accuracy of face images generated by GAN with improved ConvNeXt （I-ConvNeXt） network structure reaches 99.405%， with an average accuracy improvement of 1.455 percentage points compared with the original ConvNeXt algorithm. The results validate the feasibility and reasonableness of the proposed scheme.

Key words: Key words: generative adversarial network, attention mechanism, color features, generated face image, multi-channel input

CLC Number:

TP391

XIAO Mengsi1, WU Jianbin1, TU Yameng1, YUAN Linfeng2. GAN-generated Fake Images Recognition Based on Improved ConvNeXt[J]. Computer and Modernization, 2024, 0(04): 38-42.

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