Digital image splicing detection technique using optimal threshold based local ternary pattern

Digital images were considered as authentic proof of evidence some years ago but advancement in technology has made image tampering an easy task for every user. Investigation of the digital images for forgery detection, and authenticate their genuineness is need of the hour. To address this issue, the paper proposes a new block-based technique for image splicing detection. In this technique, first the image is converted to YCbCr format and chrominance component of the image is extracted. This component is segmented in overlapping blocks to extract local features. The paper proposes to use a new texture descriptor named as otsu based enhanced local ternary pattern (OELTP) for feature extraction from these blocks. OELTP uses an optimal threshold value to improve the enhanced local ternary pattern (ELTP) texture descriptor, for better detection of image forgery. Further, the paper proposes to use energy for reducing dimensionality of features, instead of using complex computations as used in earlier techniques. Finally, the features are sorted for speedy classification and fed to support vector machine (SVM) for labelling the images either as authentic or forged. The proposed technique has been tested on varying groups of data from the benchmark dataset(s) and has achieved an accuracy upto 98.25%. To demonstrate the superiority of proposed technique, results are also compared with the state-of-the-art techniques.