Image steganalysis method based on hybrid deep learning framework

By constructing a hybrid deep learning framework that combines a JPEG steganalysis rich model and a deep convolutional neural network, and employing multi-stage feature extraction and attention mechanisms, the problem of low detection accuracy in JPEG and color image steganalysis is solved, achieving efficient detection in complex scenarios.

CN121531074APending Publication Date: 2026-02-13NANJING XIAOZHUANG UNIV
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Patent Information

Application Number
CN202511669281.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-14
Publication Date
2026-02-13

AI Technical Summary

Technical Problem

Existing technologies struggle to effectively detect steganography in JPEG and color images, and deep learning-based methods suffer from low accuracy in real-world scenarios, failing to address data source mismatch issues in complex environments.

Method used

A hybrid deep learning framework is constructed, combining a JPEG steganalysis rich model and a deep convolutional neural network. It adopts a multi-stage feature extraction and attention mechanism, including preprocessing, feature extraction and feature classification stages, and introduces central difference convolution and Swish activation function to optimize network performance.

Benefits of technology

It significantly improves the accuracy of steganalysis detection for JPEG and color images, enhances the model's generalization ability, and adapts to steganalysis tasks in complex scenarios.

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Abstract

An image steganography analysis method based on a hybrid deep learning framework combines the feature extraction advantage of a JPEG steganography rich model and the integratable advantage of a deep learning model to construct a multi-stage hybrid deep learning JPEG steganography analysis framework, and the framework has expandability and can be applied to image steganography analysis. The latest deep learning steganalysis network can be added to the framework. Meanwhile, the steganalysis problem under various complex scenes such as data source mismatching can also be adapted; a color image steganalysis CNN trunk model formed by three stages of preprocessing, feature extraction and feature classification is designed, and a space attention mechanism and a channel attention mechanism are added in a trunk stream, so that the detection accuracy is improved; a new algorithm optimization module is designed based on the framework, the network structure is adjusted, and the generalization ability, convergence performance, detection accuracy and robustness of the model are further improved.
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