隐写分析技术
计算机科学
人工智能
隐写工具
模式识别(心理学)
隐写术
数字图像
过程(计算)
信息隐藏
数据挖掘
图像(数学)
图像处理
特征提取
JPEG格式
计算机视觉
特征(语言学)
领域(数学分析)
卷积神经网络
支持向量机
嵌入
离散余弦变换
像素
操作系统
作者
Junfu Chen,Zhangjie Fu,Xingming Sun,Enlu Li
标识
DOI:10.1007/978-3-030-87355-4_53
摘要
Steganography is a technology that modifies complex regions of digital images to embed secret messages for the purpose of covert communication, while steganalysis is to detect whether secret messages are hidden in a digital image or not. However, the emergence of content-adaptive steganography such as S-UNIWARD prioritizes the embedding of secret messages in areas of textural complexity of images by embedding probability map guidelines. Such ways dramatically improve the security of steganography and impede the process of image steganalysis. Most of the existing steganalysis studies are aimed at improving the network structure to enhance the detection performance of the model, without considering the generation of embedding probability maps which can guide the training of the network model, eliminate some unnecessary distractions, shorten the training time and improve the final detection accuracy simultaneously. Therefore, how to obtain embedded probability maps and use them effectively becomes an important challenge in the field of steganalysis. In this paper, to solve the above problem we propose a content-adaptive lightweight network to implement an embedded probability map combined with steganalysis. Our steganalysis model includes two parts: embedding probability maps generation module and features processing module, which is trained Separately. The generation module adopts the basic framework and modifies the model to make it more suitable for steganography. In the features processing module, we adopt a pseudo-siamese architecture to manipulate two different input images. Next, we use the attention mechanism to assign weights to channel parameters. Finally, We use a simple data augmentation method to enhance our training dataset and improve final performance. Because our proposed model incorporates embedded probability maps as guidelines, experiments show that our proposed CNet has faster convergence speed, higher detection accuracy, and better robustness compared to networks such as Yedroudj-Net, SRNet, and Zhu-Net in the spatial domain.
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