Abstract
Image splicing is a common operation in image forgery. Different techniques of image splicing detection have been utilized to regain people’s trust. This study introduces a texture enhancement technique involving the use of fractional differential masks based on the Machado entropy. The masks slide over the tampered image, and each pixel of the tampered image is convolved with the fractional mask weight window on eight directions. Consequently, the fractional differential texture descriptors are extracted using the gray-level co-occurrence matrix for image splicing detection. The support vector machine is used as a classifier that distinguishes between authentic and spliced images. Results prove that the achieved improvements of the proposed algorithm are compatible with other splicing detection methods.
Highlights
The detection of possible image manipulation is an important challenge in digital image forensics.Digital image forensics primarily aims to detect and analyze facts concealed behind a digital image.Image manipulation or tampering may be performed through image splicing, retouching, healing, copying-moving, and blurring
Moghaddasi et al [8] proposed an approach based on statistical features obtained from the run-length method and on image edge statistics from the blind image splicing detection method
We develop new fractional differential texture descriptors based on the Machado entropy
Summary
The detection of possible image manipulation is an important challenge in digital image forensics. One of the proposals involve establishing a natural image model for splicing detection by applying statistical feature extraction methods, including moments of characteristic functions of wavelet sub-bands and the Markov transition probabilities of the difference between 2D arrays and 2D arrays of multi-size block discrete cosine transform (MBDCT). Their results presented a promising improvement on image splicing detection accuracy. Moghaddasi et al [8] proposed an approach based on statistical features obtained from the run-length method and on image edge statistics from the blind image splicing detection method.
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