AI-Generated Image Detection Method Integrating Light-Shadow Sensitive Features and Kolmogorov-Arnold Representation Theorem

DENG Qiao; JIANG Lin; LIU Le-xin; TANG Lü-xin; YANG Ying-li

doi:10.12263/DZXB.20250250

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AI-Generated Image Detection Method Integrating Light-Shadow Sensitive Features and Kolmogorov-Arnold Representation Theorem

PAPERS | 更新时间：2026-02-10

- AI-Generated Image Detection Method Integrating Light-Shadow Sensitive Features and Kolmogorov-Arnold Representation Theorem
- ACTA ELECTRONICA SINICA Vol. 53, Issue 11, Pages: 4077-4090(2025)
- 作者机构：
  
  1.湖南工商大学人工智能与先进计算学院，湖南长沙 410000
  2.湘江实验室，湖南长沙 410000
- 作者简介：
- 基金信息：
  
  Scientific Research Project of Hunan Provincial Department of Education(22A0441);Major Project of Xiangjiang Laboratory(23XJ01003;23XJ01009)
- DOI：10.12263/DZXB.20250250
  CLC： TP391;
- Received：02 April 2025，
  
  Accepted：22 May 2025，
  
  Published：25 November 2025
- 稿件说明：
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邓巧, 姜林, 刘乐新, 等. 融合光影敏感特征及K-A表示定理的AI生成图像鉴别方法[J]. 电子学报, 2025, 53(11): 4077-4090.

DENG Qiao, JIANG Lin, LIU Le-xin, et al. AI-Generated Image Detection Method Integrating Light-Shadow Sensitive Features and Kolmogorov-Arnold Representation Theorem[J]. Acta Electronica Sinica, 2025, 53(11): 4077-4090.
邓巧, 姜林, 刘乐新, 等. 融合光影敏感特征及K-A表示定理的AI生成图像鉴别方法[J]. 电子学报, 2025, 53(11): 4077-4090. DOI：10.12263/DZXB.20250250

DENG Qiao, JIANG Lin, LIU Le-xin, et al. AI-Generated Image Detection Method Integrating Light-Shadow Sensitive Features and Kolmogorov-Arnold Representation Theorem[J]. Acta Electronica Sinica, 2025, 53(11): 4077-4090. DOI：10.12263/DZXB.20250250

摘要

人工智能（Artificial Intelligence，AI）生成图像技术发展迅猛，高逼真内容对网络安全与社会信任构成重大威胁，而人类自主鉴别准确率仅约59%，接近随机猜测水平.现有检测方法普遍存在性能有限、跨模型泛化能力不足等问题，尤其无法有效捕捉生成图像中物理光照的不一致性.为此，本文提出融合光影敏感特征及Kolmogorov-Arnold（K-A）表示定理的特征融合鉴别方法（Light-enhanced Kolmogorov-Arnold Networks，L-KAN）.在红绿蓝三原色（Red、Green、Blue，RGB）语义特征、频域特征和边缘特征的基础上，构建光影敏感特征.该特征通过整体光照分布、阴影面积及方向和多尺度光照梯度特性，捕捉生成图像中的光照异常.引入K-A表示定理进行特征融合，通过内外层函数协同作用，在保证特征互补性的同时有效抑制特征冗余.在3组公开数据集上，与9种先进方法进行对比，所提方法平均分类准确率均有显著提升.

Abstract

The rapid advancement of artificial intelligence (AI)-generated image technologies poses significant threats to cybersecurity and public trust

as human visual detection accuracy remains as low as 59%

close to random guessing. Existing detection methods suffer from limited performance and poor generalization across generative models

particularly struggling to capture physical inconsistencies in illumination. To address this gap

we propose L-KAN (Light-enhanced Kolmogorov-Arnold Networks)

a novel detection framework that integrates illumination-sensitive features with the Kolmogorov-Arnold (K-A) representation theorem. Building upon red-green-blue (RGB) semantics

frequency-domain cues

and edge information

we construct physically grounded features that encode global illumination distribution

shadow geometry

and multi-scale illumination gradients to expose lighting inconsistencies in synthetic images. Leveraging the K-A theorem for feature fusion

ours method synergizes inner and outer functions to enhance feature complementarity while suppressing redundancy. Experimental results on three public datasets demonstrate that L-KAN achieves a competitive performance compared with the state of the art methods.

关键词

Keywords

references

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