In this paper, we show that methods detecting multiple change points in a discrete distribution of variables
can play an effective role in identifying image tampering. Methods analyzing change points deal with detecting
abrupt changes in the characteristics of signals. Methods dealing with detecting image tampering isolate a subset
of the given image that is significantly different from the rest. Apparently, both groups of methods have similar
goals and thus there might be an interesting synergy between these two research fields. Change points detection
algorithms can help in automatically detecting altered parts of digital images without any previous training or
complicated threshold settings.
Geometric transformations such as scaling or rotation are common tools employed by forgery creators. These
procedures are typically based on a resampling and interpolation step. The interpolation process brings specific
periodic properties into the image. In this paper, we show how to detect these properties. Our aim is to detect
all possible geometric transformations in the image being investigated. Furthermore, as the proposed method, as
well as other existing detectors, is sensitive to noise, we also briefly show a simple method capable of detecting
image noise inconsistencies. Noise is a common tool used to conceal the traces of tampering.