Cross-stream attention enhanced central difference convolutional network for CG image detection

HUANG Jinkun; HUANG Yuanhang; HUANG Wenmin; LUO Weiqi

doi:10.11959/j.issn.2096-109x.2024083

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Cross-stream attention enhanced central difference convolutional network for CG image detection

Papers | 更新时间：2025-02-06

- Cross-stream attention enhanced central difference convolutional network for CG image detection
- Chinese Journal of Network and Information Security Vol. 10, Issue 6, Pages: 96-108(2024)
- 作者机构：
  
  1.中山大学计算机学院，广东广州 510006
  2.广东省信息安全技术重点实验室，广东广州 510006
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62472458)
- DOI：10.11959/j.issn.2096-109x.2024083
  CLC： TP391
- Received：05 April 2024，
  
  Revised：2024-08-11，
  
  Published：25 December 2024
- 稿件说明：
移动端阅览
黄锦坤, 黄远航, 黄文敏, 等. 基于跨流注意力增强中心差分卷积网络的CG图像检测[J]. 网络与信息安全学报, 2024, 10(6): 96-108.

HUANG J K, HUANG Y H, HUANG W M, et al. Cross-stream attention enhanced central difference convolutional network for CG image detection[J]. Chinese Journal of Network and Information Security, 2024, 10(6): 96-108.
黄锦坤, 黄远航, 黄文敏, 等. 基于跨流注意力增强中心差分卷积网络的CG图像检测[J]. 网络与信息安全学报, 2024, 10(6): 96-108. DOI： 10.11959/j.issn.2096-109x.2024083.

HUANG J K, HUANG Y H, HUANG W M, et al. Cross-stream attention enhanced central difference convolutional network for CG image detection[J]. Chinese Journal of Network and Information Security, 2024, 10(6): 96-108. DOI： 10.11959/j.issn.2096-109x.2024083.

摘要

随着计算机图形学（computer graphics

CG）技术在图像生成领域的日益成熟，创造的图像逼真程度大幅提升。这些技术虽然在日常生活中被广泛应用并带来诸多便利，但同时也有着许多安全隐患，如果使用CG技术生成的伪造图像被恶意使用，在互联网、社交媒体上广泛传播，则可能对个人和企业权益造成损害。提出了一种创新的跨流注意力增强中心差分卷积网络，致力于提高CG图像检测的准确性。模型中构建了一个双流结构，旨在分别从图像中抽取语义特征和非语义的残差纹理特征。每个流中的普通卷积层被中心差分卷积所替代，这一改进使模型能同时提取图像中的像素强度信息和像素梯度信息。此外，通过引入一个跨流注意力增强模块，该模型在全局层面上增强了特征提取能力，并促进了两个特征流之间的互补。实验结果证明，基于跨流注意力增强中心差分卷积网络的CG图像检测方法相比现有方法具有更优的性能。此外，一系列消融实验进一步验证了所提模型设计的合理性。

Abstract

With the maturation of computer graphics (CG) technology in the field of image generation

the realism of created images has been improved significantly. Although these technologies are widely used in daily life and bring many conveniences

they also come with many security risks. If forged images generated using CG technology are maliciously used and widely spread on the Internet and social media

they may harm the rights of individuals and enterprises. Therefore

an innovative cross-stream attention enhanced central difference convolutional network was proposed

aiming at improving the accuracy of CG image detection. A dual-stream structure was constructed in the model

in order to extract semantic features and non-semantic residual texture features from the image. Vanilla convolutional layers in each stream were replaced by central difference convolutions

which allowed the model to simultaneously extract pixel intensity information and pixel gradient information from the image. Furthermore

by introducing a cross-stream attention enhancement module

the model enhanced feature extraction capability at the global level and promoted complementarity between the two feature streams. Experimental results demonstrate that this method outperforms existing methods. Additionally

a series of ablation experiments further verify the rationality of the proposed model design.

关键词

Keywords

references

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