Image recognition method, device and equipment based on content security, and storage medium

By extracting multi-scale texture features and texture correlations from images using a multi-scale texture perception model, the robustness and generalization of existing computer-generated image recognition methods are addressed, achieving efficient and accurate image recognition.

CN115311475BActive Publication Date: 2026-01-27INST OF AUTOMATION CHINESE ACAD OF SCI +1
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Patent Information

Application Number
CN202210752086.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-06-28
Publication Date
2026-01-27
Estimated Expiration
2042-06-28

AI Technical Summary

Technical Problem

In existing technologies, computer-generated image identification methods lack consideration for differences in feature details, resulting in poor model robustness and generalization, making it difficult to adapt to large-scale image datasets, and requiring a high level of expertise.

Method used

A multi-scale texture perception model is adopted to extract multi-scale texture features of the image to be identified, determine the texture correlation between any two channels, and perform image type identification by combining texture enhancement features and fusion features.

Benefits of technology

It improves the accuracy and robustness of image recognition, expands the applicability of the model, reduces the professional requirements, and enhances the ability to recognize detailed information in images.

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Abstract

The application provides a content security-based image recognition method and device, equipment and a storage medium, and relates to the technical field of computer vision, and the method comprises the following steps: acquiring an image to be recognized; inputting the image to be recognized into a multi-scale texture perception model to output an image category recognition result for representing whether the image is fake; wherein the multi-scale texture perception model is obtained based on image sample data and corresponding image category labels, and the multi-scale texture perception model is used for performing category recognition on the image to be recognized based on the correlation between any two channels of multi-scale texture features of the image to be recognized. The application can combine the multi-scale texture features and the subtle differences between different channels of the features to improve the image category recognition accuracy, and improve the robustness and generalization.
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