CTForensics: A Comprehensive Dataset and Method for AI-Generated CT Image Detection

TL;DR

This paper introduces CTForensics, a comprehensive dataset for evaluating AI-generated CT image detection, and proposes ESF-CTFD, a CNN-based method that effectively captures forgery cues across multiple domains, outperforming existing approaches.

Contribution

The paper provides the first large-scale dataset for CT forgery detection and develops a novel neural network that integrates spatial and frequency domain features for improved accuracy.

Findings

ESF-CTFD outperforms existing detection methods.

The dataset enables better evaluation of model generalization.

The method demonstrates robustness across various CT generative models.

Abstract

With the rapid development of generative AI in medical imaging, synthetic Computed Tomography (CT) images have demonstrated great potential in applications such as data augmentation and clinical diagnosis, but they also introduce serious security risks. Despite the increasing security concerns, existing studies on CT forgery detection are still limited and fail to adequately address real-world challenges. These limitations are mainly reflected in two aspects: the absence of datasets that can effectively evaluate model generalization to reflect the real-world application requirements, and the reliance on detection methods designed for natural images that are insensitive to CT-specific forgery artifacts. In this view, we propose CTForensics, a comprehensive dataset designed to systematically evaluate the generalization capability of CT forgery detection methods, which includes ten diverse CT generative methods. Moreover, we introduce the Enhanced Spatial-Frequency CT Forgery Detector (ESF-CTFD), an efficient CNN-based neural network that captures forgery cues across the wavelet, spatial, and frequency domains. First, it transforms the input CT image into three scales and extracts features at each scale via the Wavelet-Enhanced Central Stem. Then, starting from the largest-scale features, the Spatial Process Block gradually performs feature fusion with the smaller-scale ones. Finally, the Frequency Process Block learns frequency-domain information for predicting the final results. Experiments demonstrate that ESF-CTFD consistently outperforms existing methods and exhibits superior generalization across different CT generative models.