Identification of copy-move and splicing based forgeries using advanced SURF and revised template matching

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Various image tampering detection approaches are used to find the variations or inconsistencies in statistical image features. But still these techniques lack behind to identify copy-move and splicing based manipulations. The manipulation in digital data encourages the crimes, particularly in the domain of image processing and computer vision-based applications. Therefore, to find image forgeries, new method needs to be designed so that originality of data is authenticated in the court of law or jurisdiction. To achieve, a pixel based forgery detection framework for copy-move and splicing based forgeries is suggested in this paper. Initially, pre-processing over image data is performed to enhance the textural information. The proposed system estimates various features using enhanced SURF and template matching for the identification of fake image regions. The relevant key parameters are estimated and compared with the calculated threshold value. The evaluation is carried out using CASIA forged image dataset. The results are evaluated and compared with other existing methods through a comprehensive set of experiments. The enhanced SURF method produces a forgery detection accuracy of 97%, while template matching gives 100% forgery detection. As a whole system, the accuracy is 97.5%. Thus, the demonstrated result shows that the proposed framework attains considerably more detection accuracy compared to other state-of-art techniques.