Detection of Resampling Supplemented with Noise Inconsistencies Analysis for Image Forensics

When two or more images are spliced together, to create high quality and consistent image forgeries, almost always geometric transformations such as scaling or rotation are needed. These procedures are typically based on a resampling and interpolation step. In this paper, we show a blind method capable of finding traces of resampling and interpolation. Unfortunately, the proposed method, as well as other existing interpolation/resampling detectors, is very sensitive to noise. The noise degradation causes that detectable periodic correlations brought into the signal by the interpolation process become corrupted and difficult to detect. Therefore, we also propose a simple method capable of dividing an investigated image into various partitions with homogenous noise levels. Adding locally random noise may cause inconsistencies in the imagepsilas noise. Hence, the detection of various noise levels in an image may signify tampering.