Detecting similarities and differences in images using the PFF and LGG approaches

This paper presents two methods for comparison of images and evaluation of visibility of artifacts due to hidden information, changes or noise. The first method is based on pixel flow functions (PFF) able to detect changes in images by projecting the pixel values vertically, horizontally and diagonally. These projections create "functions" related with the average values of pixels summarized horizontally, vertically and diagonally. These functions represent image signatures. The comparison of image signatures defines differences in images. The second method is based on a heuristic graph model, known as local-global graph (LGG), for evaluating visibility of modifications in digital images. The LGG is based on segmentation and comparing the segments while thresholding the differences in their attributes. The methods have been implemented in C++ and their performance is presented.