面向载体选择隐写的训练样本优化隐写分析方法

doi:10.3969/j.issn.0255-8297.2026.05.010

Abstract

Abstract: To address the challenge posed by cover selection steganography to traditional steganalysis, this paper proposed an improved steganalytic method based on training sample optimization. Traditional handcrafted feature-based steganalysis usually trains ensemble classifiers using paired clean-stego image pairs during the training phase. Given the characteristic that steganographers deliberately select suitable images for embedding in cover selection steganography, this paper optimized the method of training sample construction by replacing the traditional paired samples with disjoint clean and stego image subsets, making the training data more consistent with real-world steganographic scenarios. Therefore, an ensemble classifier more sensitive to cover selection behavior was trained. Experimental results have shown that the proposed method can effectively improve the detection accuracy of cover selection steganography.

Key words: steganography, steganalysis, cover selection, feature extraction, digital image

CLC Number:

TP309

CAO Haiteng, WANG Zichi, ZHANG Xinpeng. Steganalytic Method Based on Training Sample Optimization for Cover Selection Steganography[J]. Journal of Applied Sciences, 2026, 44(5): 838-845.

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URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2026.05.010

https://www.jas.shu.edu.cn/EN/Y2026/V44/I5/838

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Steganalytic Method Based on Training Sample Optimization for Cover Selection Steganography

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