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Pragmatical Investigation of Frequency-Domain and Spatial-Structure Based Image Forgery Detection Methods

Digital image forgery detection has turned out to be an influential area of research in the recent couple of years due to the growing number of instances involving modifications to an image. Copy-paste forgery is the most typical form of altering an image that can even be performed by an amateur with the help of software tools available on the web. Detecting such forgeries have become a major problem of the time. In this paper, detection of various copy-paste forgeries using frequency transform based and spatial-structure based features are demonstrated on three different benchmark datasets, viz., CASIA v1.0, CASIA v2.0 and COVERAGE dataset. The effectiveness of each feature for forgery detection is validated through experimentations in MATLAB platform on above said datasets in terms of accuracy, precision and F1-score. The threshold calibration is performed on the basis of F1 score and the results are depicted in terms of ROC curves. It has been concluded that out of DCT, DWT and LBP features, DCT gave best results. The frequency domain features do not work well when the forgeries are done with the help of post-processing operations whereas LBP features do not work well in the presence of geometrical transformations. Further, these outcomes will be helpful in creating ensembles of various detection algorithms.