Optimal detector for camera model identification based on an accurate model of DCT coefficients

T. H. Thai, R. Cogranne, F. Retraint
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引用次数: 2

Abstract

The goal of this paper is to design a statistical test for the camera model identification problem. The approach is based on the state-of-the-art model of Discret Cosine Transform (DCT) coefficients to capture their statistical difference, which jointly results from different sensor noises and in-camera processing algorithms. The noise model parameters are considered as camera fingerprint to identify camera models. The camera model identification problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, this paper studies the optimal detector given by the Likelihood Ratio Test (LRT) and analytically establishes its statistical performances. In practice, a Generalized LRT is designed to deal with the difficulty of unknown parameters such that it can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated database and natural JPEG images highlight the relevance of the proposed approach.
基于精确DCT系数模型的相机模型识别优化检测器
本文的目的是设计一个相机模型识别问题的统计检验。该方法基于最先进的离散余弦变换(DCT)系数模型来捕获它们的统计差异,这些差异是由不同的传感器噪声和相机内处理算法共同产生的。将噪声模型参数作为相机指纹进行相机型号识别。摄像机模型识别问题是在假设检验理论的框架下进行的。在所有模型参数完全已知的理想情况下,本文研究了似然比检验(LRT)给出的最优检测器,并分析建立了其统计性能。在实践中,设计了一种广义LRT来处理未知参数的困难,使其能够满足规定的虚警概率,同时保证较高的检测性能。在模拟数据库和自然JPEG图像上的数值结果突出了该方法的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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