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首页> 外文会议>IEEE International Conference on Signal Processing, Communications and Computing >Selection-Channel-Aware Deep Neural Network to Detect Motion Vector Embedding of HEVC Videos
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Selection-Channel-Aware Deep Neural Network to Detect Motion Vector Embedding of HEVC Videos

机译：选择通道感知的深度神经网络检测HEVC视频的运动矢量嵌入

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摘要

It is well established that using the selection channel, the probabilities with which the elements in cover are modified during message embedding, would improve the performance of steganalysis. Most video steganographical algorithms embed secret messages in the compressed domain by modifying the motion vectors having less impact on video visual quality, which can be considered as a form of selection channel. Recently, deep neural networks have been rapidly developed for multimedia steganalysis. Although there have been some selection-channel-aware networks for image steganalysis, they cannot be simply extended to video steganalysis because there are great differences between image and video steganographic modification. To our best knowledge, there have been no selection-channel-aware networks for video steganalysis in literature. In this article, we propose a selection-channel-aware deep neural network for video steganalysis. Considering that video structure is quite different from that of image, we focus on the construction of input data matrix for deep convolutional neural network, the definition of probability for motion vector modification, and the network structure of using the selection channel knowledge. Experimental results have demonstrated that the proposed method benefits from selection channel and has satisfactory performance on testing HEVC videos.
机译：公认的是，使用选择通道，在消息嵌入期间修改封面元素的概率将提高隐写分析的性能。大多数视频隐秘算法通过修改对视频视觉质量影响较小的运动矢量将秘密消息嵌入压缩域中，这可以视为选择通道的一种形式。近来，已经快速开发了用于多媒体隐写分析的深度神经网络。尽管存在一些用于图像隐写分析的选择通道感知网络，但是由于图像隐写和视频隐写修改之间存在很大差异，因此不能将它们简单地扩展到视频隐写分析。据我们所知，文献中还没有用于视频隐写分析的选择通道感知网络。在本文中，我们提出了一种用于视频隐写分析的选择通道感知深度神经网络。考虑到视频结构与图像结构完全不同，我们重点研究深度卷积神经网络的输入数据矩阵的构造，运动矢量修改概率的定义以及使用选择通道知识的网络结构。实验结果表明，该方法受益于选择渠道，在HEVC视频测试中具有令人满意的性能。

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