High-dimensional feature extraction based on co-occurrence matrix improves the detection performance of steganalysis, but it is difficult to be realized for massive image data by an analyzer with limited computational ability. We solve this problem by verifiable outsourcing computation, which allows a computationally weak client to outsource the evaluation of a function to a powerful but untrusted server. In this paper, we propose a verifiable outsourcing scheme of feature extraction based on co-occurrence matrix with single untrusted cloud server. The original images are protected from the server by using a projection of one to many with trapdoor, which can be realized by a symmetric probabilistic encryption scheme we present. The analyzer can obtain true results of feature extraction and detect any failure with a probability of 1 if the server misbehaves. Finally, we provide the simulations on the outsourcing of extracting ccJRM features in cloud computing. The theory analysis and experiment result also show that the proposed outsourcing scheme could greatly decrease the computation cost of the analyzer without exposure of the original images and extraction results.

中文翻译：

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云计算中如何安全高效地提取基于共生矩阵的图像特征

基于共生矩阵的高维特征提取提高了隐写分析的检测性能，但对于海量图像数据，计算能力有限的分析器难以实现。我们通过可验证的外包计算解决了这个问题，这允许计算能力弱的客户端将函数的评估外包给强大但不受信任的服务器。在本文中，我们提出了一种基于共现矩阵与单个不可信云服务器的可验证特征提取外包方案。通过使用带有陷门的一对多投影来保护原始图像不受服务器的影响，这可以通过我们提出的对称概率加密方案来实现。如果服务器行为不端，分析器可以获得特征提取的真实结果，并以 1 的概率检测到任何故障。最后，我们对云计算中提取ccJRM特征的外包进行了模拟。理论分析和实验结果也表明，所提出的外包方案可以在不暴露原始图像和提取结果的情况下，大大降低分析仪的计算成本。