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Hidden information detection based on quantized Laplacian distribution

机译:基于量化拉普拉斯分布的隐藏信息检测

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The goal of this paper is to propose the optimal statistical test based on the modeling of discrete cosine transform (DCT) coefficients with a quantified Laplacian distribution. This paper focuses on the detection of hidden information embedded in bits of the DCT coefficients of a JPEG image. This problem is difficult, in terms of statistical decision, for two main reasons: first, the number of DCT coefficients used to conceal the hidden bits is random; second, the JPEG image compression induces a strong quantization of DCT coefficients. The proposed test explicitly takes into account the randomness of the number of DCT coefficients used. It maximizes the probability of hidden information detection by ensuring a prescribed level of false alarm.
机译:本文的目的是基于具有量化拉普拉斯分布的离散余弦变换(DCT)系数的建模,提出最佳统计检验。本文着重于检测嵌入在JPEG图像DCT系数位中的隐藏信息。就统计决策而言,此问题很困难,主要有两个原因:首先,用于隐藏隐藏位的DCT系数的数量是随机的;第二,JPEG图像压缩引起DCT系数的强烈量化。拟议的测试明确考虑了所用DCT系数数量的随机性。通过确保规定水平的错误警报,它最大化了隐藏信息检测的可能性。

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