Information-theoretic Bounds of Resampling Forensics: New Evidence for Traces Beyond Cyclostationarity
Information-theoretic Bounds of Resampling Forensics: New Evidence for Traces Beyond Cyclostationarity
复制标题
重采样取证的信息论界限：超越循环平稳性痕迹的新证据
DOI：
10.1145/3082031.3083233
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
R. Böhme
中科院分区：
文献类型：
--
作者：
C. Pasquini;R. Böhme
虽然已经提出了几种方法来检测多媒体信号中的重采样操作和估计重采样因子，但这种取证任务的基本限制留下了悬而未决的研究问题。在这项工作中，我们探讨了下采样操作作为所用参数的函数在一维信号的统计中引入的影响。在广义平稳一阶自回归信号模型的情况下，我们利用Kullback-Leibler散度(KLD)来量化原始信号和其下采样信号之间的统计距离。推导了不同信号参数、重采样因子和内插核的KLD值，从而预测了在每种情况下可实现的假设可区分性。我们的分析表明，由于原始信号的局部相关结构，在强下采样的情况下可以意外地检测到。此外，由于现有的检测方法通常利用重采样信号的循环平稳性，我们还解决了直接利用被调查信号的样本自协方差来估计自协方差值的情况。在所考虑的假设下，Wishart分布对信号段的样本协方差矩阵进行建模，并推导出不同假设下的KLD。
Although several methods have been proposed for the detection of resampling operations in multimedia signals and the estimation of the resampling factor, the fundamental limits for this forensic task leave open research questions. In this work, we explore the effects that a downsampling operation introduces in the statistics of a 1D signal as a function of the parameters used. We quantify the statistical distance between an original signal and its downsampled version by means of the Kullback-Leibler Divergence (KLD) in case of a wide-sense stationary 1st-order autoregressive signal model. Values of the KLD are derived for different signal parameters, resampling factors and interpolation kernels, thus predicting the achievable hypothesis distinguishability in each case. Our analysis reveals unexpected detectability in case of strong downsampling due to the local correlation structure of the original signal. Moreover, since existing detection methods generally leverage the cyclostationarity of resampled signals, we also address the case where the autocovariance values are estimated directly by means of the sample autocovariance from the signal under investigation. Under the considered assumptions, the Wishart distribution models the sample covariance matrix of a signal segment and the KLD under different hypotheses is derived.
登录
查看更多内容
重采样信号的集合成员身份识别
DOI：
10.1109/wifs.2013.6707810
发表时间：
2013
期刊：
2013 IEEE International Workshop on Information Forensics and Security (WIFS)
影响因子：
--
作者：
David Vázquez;Pedro Comesaña Alfaro;F. Pérez
通讯作者：
F. Pérez
DOI：
10.1109/tsp.1993.193133
发表时间：
1993
期刊：
IEEE Trans. Signal Process.
影响因子：
--
作者：
V. Sathe;P. Vaidyanathan
通讯作者：
P. Vaidyanathan
影响因子：
8.2
作者：
F. Nielsen;R. Nock
通讯作者：
R. Nock
重采样因子的 ML 估计
DOI：
10.1109/wifs.2012.6412650
发表时间：
2012
期刊：
2012 IEEE International Workshop on Information Forensics and Security (WIFS)
影响因子：
--
作者：
David Vázquez;Pedro Comesaña Alfaro
通讯作者：
Pedro Comesaña Alfaro