Splicing Localization Based on Noise Level Inconsistencies in Residuals of Color Channel Differences
P N R L Chandra Sekhar1, T N Shankar2
1PNRL Chandra Sekhar, Department of Computer Science & Engineering, Koneru Lakshmaih Educational Foundation, Vaddeswaram, Guntur (AP), India.
2T N Shankar, Department of Computer Science & Engineering, Koneru Lakshmaih Educational Foundation, Vaddeswaram, Guntur, AP, India.
Manuscript received on 8 August 2019. | Revised Manuscript received on 16 August 2019. | Manuscript published on 30 September 2019. | PP: 764-7769 | Volume-8 Issue-3 September 2019 | Retrieval Number: C3999098319/19©BEIESP | DOI: 10.35940/ijrte.C3999.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the rapid usage of networking sites there is an enormous increase in image sharing over the internet. At the same time altering or tempering images has become much easier with the availability of photo editing software. Splicing is one of the tempering method, where an object from one image is copied and pasted into another image, is often used with the aim of either getting attention for fun or misleading the general masses. Thus, authenticity of images shared on internet is debatable. Active research is going on in the field of image forensics in order to examine the trustworthiness of the images. Amongst several techniques available for dealing with image splicing, the statistical based methods are gaining attention in research community as it uses image’s local statistics. We propose a simple and effective method based on noise inconsistencies in residuals of Color channel difference for forensic analysis to localize the splicing image forgery. First the image is decomposed in to super pixels and extracted in regular shapes. From each super pixel, three color channel differences are extracted and noise level is estimated on the residual. Finally, the super pixels are clustered into two groups using Farthest Distributed Centroids Clustering (FDCC) method for classifying superpixel as tampered or original. The experimental results show the simplicity and effectiveness of the proposed method over the state of the art.
Keywords: Color Channel Differences, residual images, Superpixels, Splicing Localization, Clustering.
Scope of the Article: Vision-based applications