Detection of Linear and Cubic Interpolation in JPEG Compressed Images
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue
Proceedings of the 10th ACM workshop on Multimedia and security
Multimedia Forensics Is Not Computer Forensics
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
A new approach for JPEG resize and image splicing detection
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
The 'Dresden Image Database' for benchmarking digital image forensics
Proceedings of the 2010 ACM Symposium on Applied Computing
Statistical tools for digital forensics
IH'04 Proceedings of the 6th international conference on Information Hiding
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
Estimation of linear transformation by analyzing the periodicity of interpolation
Pattern Recognition Letters
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This paper adds a new perspective to the analysis and detection of periodic interpolation artifacts in resized digital images. Instead of relying on a single, global predictor, we discuss how the specific structure of resized images can be explicitly modeled by a series of linear predictors. Characteristic periodic correlations between neighboring pixels are then measured in the estimated predictor coefficients itself. Experimental results on a large database of images suggest a superior detection performance compared to state-of-the-art methods.