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Weizheng Jin¹, Hao Luo¹, Yunqi Tang¹

¹School of Criminal Investigation, People's Public Security University of China, Beijing, China.

Journal of Forensic Sciences

|March 29, 2025

Summary

This study presents a novel method for detecting AI-generated images, achieving over 99% accuracy. The research aims to ensure judicial fairness by providing reliable tools for identifying artificial intelligence-created visual evidence.

Keywords:

AI‐generated image image detection image identification score‐based likelihood ratio method

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Area of Science:

Computer Science
Artificial Intelligence
Forensic Science

Background:

Advancements in diffusion models enable highly realistic AI-generated images.
The use of AI-generated images as evidence poses a significant threat to judicial fairness and integrity.
Existing methods for identifying AI-generated content require robust adversarial detection techniques.

Purpose of the Study:

To develop and evaluate adversarial identification methods for AI-generated images.
To assess the reliability of AI-generated image detection models in a judicial context.
To propose a robust framework for evaluating the evidential strength of AI-generated image detection.

Main Methods:

Construction of a large dataset (18,000 images) using Craiyon V3 AI image generation software.
Implementation of a deep learning-based AI-generated image detection model.
Application of a score-based likelihood ratio method for evaluating evidence strength.

Main Results:

The proposed method achieved over 99% accuracy across multiple deep learning classifiers (Swin-Transformer, ResNet-18).
The likelihood ratio model demonstrated validity through rigorous testing, including Tippett plots.
The study successfully validated the effectiveness of the adversarial identification approach.

Conclusions:

The developed method offers a reliable and accurate solution for identifying AI-generated images.
This research provides a strong foundation for applying AI-generated image detection in judicial practice.
The findings support the use of advanced computational methods to uphold judicial fairness in the age of AI.