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

摘要/Abstract

摘要： 针对载体选择隐写对传统隐写分析方法构成的挑战，提出一种基于训练样本优化的隐写分析改进方法。传统基于手工设计特征的隐写分析，在训练阶段通常采用成对的干净-含密图像对训练集成分类器。针对隐写者在载体选择隐写中会刻意筛选适配图像进行隐写的场景特性，本文对训练样本构造方式进行优化：将传统成对样本替换为互不相交的干净图像子集与含密图像子集，使训练数据更贴合实际隐写场景，从而训练出对载体选择行为更敏感的集成分类器。实验结果表明，该方法能有效提升对载体选择隐写的检测准确率。

关键词: 隐写, 隐写分析, 载体选择, 特征提取, 数字图像

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

中图分类号:

TP309

曹海腾, 王子驰, 张新鹏. 面向载体选择隐写的训练样本优化隐写分析方法[J]. 应用科学学报, 2026, 44(5): 838-845.

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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面向载体选择隐写的训练样本优化隐写分析方法

Steganalytic Method Based on Training Sample Optimization for Cover Selection Steganography

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