The spectral correlation theory of cyclostationary time-series
Signal Processing
Cyclostationarity: half a century of research
Signal Processing
Bibliography on cyclostationarity
Signal Processing
Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue
Proceedings of the 10th ACM workshop on Multimedia and security
Exposing digital forgeries by detecting traces of resampling
IEEE Transactions on Signal Processing
Blind Authentication Using Periodic Properties of Interpolation
IEEE Transactions on Information Forensics and Security
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The knowledge of image's geometric history plays an important role in image signal compression, image registration, image retrieval and especially in image forensics. In this paper we focus on scaling and show that images that have undergone scaling contain hidden cyclostationary features. This makes possible employing the well---developed theory and efficient methods of cyclostationarity for a blind analyzing of the history of images in respect to scaling transformation. To verify this, we also propose a cyclostationarity detection method applied to our problem and show how the traces of scaling can be detected and the specific parameters of the transformation estimated. The method is based on the fact that a cyclostationary signal has a frequency spectrum correlated with a shifted version of itself. A quantitative measure of the efficiency of the method is proposed as well.