ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2019, Vol. 56 ›› Issue (10): 2250-2261.doi: 10.7544/issn1000-1239.2019.20190386

所属专题: 2019密码学与智能安全研究专题

• 信息安全 • 上一篇    下一篇

基于卷积神经网络的JPEG图像隐写分析参照图像生成方法

任魏翔,翟黎明,王丽娜,嘉炬   

  1. (空天信息安全与可信计算教育部重点实验室(武汉大学) 武汉 430072) (武汉大学国家网络安全学院 武汉 430072) (renweixiang@whu.edu.cn)
  • 出版日期: 2019-10-16
  • 基金资助: 
    国家自然科学基金重点项目(U1536204);NSFC-通用技术基础研究联合基金项目(U1836112);国家自然科学基金项目(61876134,61872275)

Reference Image Generation Algorithm for JPEG Image Steganalysis Based on Convolutional Neural Network

Ren Weixiang, Zhai Liming, Wang Lina, Jia Ju   

  1. (Key Laboratory of Aerospace Information Security and Trusted Computing(Wuhan University), Ministry of Education, Wuhan 430072) (School of Cyber Science and Engineering, Wuhan University, Wuhan 430072)
  • Online: 2019-10-16

摘要: 基于深度学习的JPEG数字图像隐写分析模型检测能力已超越基于人工设计特征隐写分析模型,但检测能力仍存在提升空间.以进一步提升JPEG隐写分析模型的检测能力为目标,借助深度学习方法,为基于深度学习的JPEG隐写分析模型提供辅助信息,从数据输入角度,探索进一步提升隐写分析模型检测能力的途径.基于卷积神经网络,构建隐写分析参照图像生成模型,对待检测图像进行变换,从而获得对应参照图像.之后,将待检测图像与对应参照图像作为隐写分析模型的输入数据,进一步挖掘待检测图像中存在的隐写分析相关信息.为验证所提出算法的有效性,进行针对JPEG自适应隐写算法的对比实验.实验结果表明:所设计的参照图像生成模型能够提升现有基于深度学习的隐写分析模型检测能力,提升效果最多可达6个百分点.

关键词: JPEG隐写分析, 辅助信息, 参照图像, 卷积神经网络, JPEG自适应隐写算法

Abstract: As the opponent of image steganography, the image steganalysis is to detect the secret message in images concealed by steganography algorithms. Recently, state-of-the-art JPEG image steganalysis schemes are changing from complex handcrafted feature-based ones to deep learning-based ones. Although the deep learning steganalysis for detecting JPEG steganography achieves great advancement, there still exists room for improvement. As it is verified that side information could promote the steganography detection accuracy, we seek the method to further improve the accuracy of content-adaptive steganography detection in JPEG domain from the perspective of side information offering for the deep learning steganalysis scheme. The proposed method utilizes convolutional neural networks to generate reference images from the input data. And the reference image is treated as the side information for the deep learning-based JPEG image steganalysis model. The proposed method can be pre-trained or trained together with the steganalysis model. Experimental results on classic content-adaptive steganography algorithms in JPEG domain named J-UNIWARD and JC-UED verifies the proposed method could enhance the detection ability compared with the deep learning steganalysis model without the aid of the proposed method to a certain extent. The proposed method could boost the detection accuracy for deep learning-based JPEG steganalysis model by 6 percentage points at most.

Key words: JPEG steganalysis, side information, reference image, convolution neural network (CNN), JPEG adaptive steganography

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