Digital image splicing detection based on approximate run length |
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Authors: | Zhongwei He Hongtao Lu |
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Affiliation: | a School of Software, Sun Yat-sen University, Guangzhou 510006, China b School of Information Science and Technology, Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou 510006, China c Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China |
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Abstract: | Image splicing is very common and fundamental in image tampering, which severely threatens the integrity and authenticity of images. As a result, the detection of image splicing is of great importance. In this paper, an approximate run length based scheme is proposed to detect this specific artifact. Firstly, the edge gradient matrix of an image is computed, and approximate run length is calculated along the edge gradient direction. Then, some features are constructed from the histogram of the approximate run length. To further improve the detection accuracy, the approximate run length is applied on the predict-error image and the reconstructed images based on DWT to obtain more features. Finally, support vector machine (SVM) is exploited to classify the authentic and spliced images using the constructed features. The experiment results demonstrate that the proposed approach can achieve a relatively high accuracy with less computational cost and fewer features when compared with other methods. |
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Keywords: | Image splicing detection Digital image forensics Approximate run length Edge detection Characteristic function |
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