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ORIGINAL ARTICLE
Year : 2019  |  Volume : 9  |  Issue : 4  |  Page : 211-220

A generalized ghost detection and segmentation method for double-joint photographic experts group compression


Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA

Correspondence Address:
Mrs. Sepideh Azarianpour
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jmss.JMSS_19_19

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Background: The versatility of digital photographs and vast usage of image processing tools have made the image manipulation accessible and ubiquitous. Thus, there is an urgent need to develop digital image forensics tools, specifically for joint photographic experts group (JPEG) format which is the most prevailing format for storing digital photographs. Existing double JPEG methods needs improvement to reduce their sensitivity to the random grid shifts which is highly common in manipulation scenario. Also, a fully automatic pipeline, in terms of segmentation followed by the classifier is still required. Methods: First, a low-pass filter (with some modifications) is used to distinguish between high-textured and low-textured areas. Then, using the inconsistency values between the quality-factors, a grayscale image, called the ghost image, is constituted. To automate the whole method, a novel segmentation method is also proposed, which extracts the ghost borders. In the last step of the proposed method, using Kolmogorov–Smirnov statistic, the distance between two separated areas (ghost area and the rest of the image) is calculated and compared with a predefined threshold to confirm the presence of forgery/authenticity. Results: In this study, a simple yet efficient algorithm to detect double-JPEG compression is proposed. This method reveals the sub-visual differences in the quality factor in the different parts of the image. Afterward, forgery borders are extracted and are used to assess authenticity score. In our experiments, the average specificity of our segmentation method exceeds 92% and the average precision is 75%. Conclusion: The final binary results for classification are compared with six state-of-the-art methods. According to several performance metrics, our method outperforms the previously proposed ones.


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