A novel method for detecting image sharpening based on local binary pattern

In image forensics, determining the image editing history plays an important role as most digital images need to be edited for various purposes. Image sharpening which aims to enhance the image edge contrast for a clear view is considered to be one of the most fundamental editing techniques. However, only a few works have been reported on the detection of image sharpening. From a perspective of texture analysis, the over-shoot artifact caused by image sharpening can be regarded as a special kind of texture modification. We also find that this kind of texture modification can be characterized by local binary patterns (LBP), which is one of the most wildly used methods for texture classification. Therefore, in this paper we propose a novel method based on LBP to detect the application of sharpening in digital image. At first, we employ Canny operator for edge detection. The rotation-invariant LBP was applied to the detected edge pixels of images for feature extraction. Then features extracted from sharpened and unsharpened images are fed into a support vector machine (SVM) classifier for classification. Experimental results on digital images with different coefficients for sharpening have demonstrated the capability of this method. Comparing with the state-of-arts, the proposed method is validated to be the one with better performance in sharpening detection.