Statistical Model-Based Detector via Texture Weight Map: Application in Re-Sampling Authentication
Statistical Model-Based Detector via Texture Weight Map: Application in Re-Sampling Authentication
复制标题
基于纹理权重图的基于统计模型的检测器：在重采样认证中的应用
DOI：
10.1109/tmm.2018.2872863
复制
发表时间：
2019-05-01
影响因子：
7.3
通讯作者：
Wu, Yiming
中科院分区：
文献类型：
--
作者：
Qiao, Tong;Shi, Ran;Wu, Yiming
The problem of authenticating a re-sampled image has been investigated over many years. Currently, however, little research proposes a statistical model-based test, resulting in that statistical performance of the resampling detector could not be completely analyzed. To fill the gap, we utilize a parametric model to expose the traces of resampling forgery, which is described with the distribution of residual noise. Afterward, we propose a statistical model describing the residual noise from a resampled image. Then, the detection problem is cast into the framework of hypothesis testing theory. By considering the image content with designing a texture weight map, two types of statistical detectors are established. In an ideal context in which all distribution parameters are perfectly known, the likelihood ratio test (LRT) is presented and its performance is theoretically established. An upper bound of the detection power can be successfully obtained from the statistical performance of an LRT. For practical use, when the distribution parameters are not known, a generalized LRT with three different maps based on estimation of parameters is established. Numerical results on simulated data and real natural images highlight the relevance of our proposed approach.
