A Fraudulent Image Detection Method Based on CTFNet

The CTFNet network combines CNN, Transformer, and frequency domain fusion networks to extract and fuse multiple features of images, solving the problem of low accuracy in fraud image detection in existing technologies and achieving more efficient fraud image recognition.

CN119580077BActive Publication Date: 2025-10-28CHONGQING UNIV OF POSTS & TELECOMM
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Patent Information

Application Number
CN202411625382.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-11-14
Publication Date
2025-10-28
Estimated Expiration
2044-11-14

AI Technical Summary

Technical Problem

Existing fraud image detection technologies struggle to accurately identify fraudulent activities in images. Traditional methods rely on the physical properties and metadata of images, while machine learning methods have low detection accuracy.

Method used

The CTFNet network is used, combined with CNN, Transformer and frequency domain fusion network to extract local features, global features and high and low frequency features of the image, and improves detection accuracy through adaptive weighted fusion.

Benefits of technology

By fusing multiple features and adaptively assigning weights, the accuracy and robustness of fraud image detection are significantly improved, while suppressing noise interference.

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Abstract

This invention relates to a fraudulent image detection method based on CTFNet, belonging to the field of fraud detection technology, and includes the following steps: S1: Constructing a fraudulent image training set and an image similarity test set; S2: Constructing a CTFNet feature extraction network to extract local features, global features, and high- and low-frequency features in the spatial domain of the two datasets respectively; S3: Calculating the cosine similarity of the features of the two datasets respectively, and obtaining the similarity threshold of the fraudulent image through adaptive weighted fusion; S4: Inputting the image to be detected into CTFNet to extract local features, global features, and high- and low-frequency features in the spatial domain of the image to be detected; S5: Calculating the cosine similarity of the features of the image to be detected and the fraudulent image training set, and obtaining the best similarity score of the image to be detected through adaptive weighted fusion; S6: Comparing the best similarity score of the image to be detected with the similarity threshold of the fraudulent image to obtain the detection result of the image to be detected.
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