FARIP: Framework for Artifact Removal for Image Processing Using JPEG

Shashidhar, T. M.; Ramesh, K.

doi:10.1007/978-3-319-91189-2_2

Cited by 2 publications

(1 citation statement)

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“…The classifying capacity is built on the amount and feature set, training of the amount and feature set training of the training sample set, where as the model validation for its accuracy is done on the test data. The conventional statistical approach as constant to the machine learning approach is developed without the separation and splitting process of the into training and testing [9,10]. The prime focus laid here is to achieve maximum possible accuracy in image forensics operation while compromising the quality of image data.…”

Section: Introductionmentioning

confidence: 99%

An efficient computational approach to balance the trade-off between image forensics and perceptual image quality

Tm¹,

Ramesh²

2019

IJECE

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The increasing trends of image processing applications play a very crucial role in the modern-day information propagation with the ease of cost effectiveness. As image transmission or broadcasting is the simplest form communication which determines easy, fastest and effective way of network resource utilization, thereby since past one decade it has gained significant attention among various research communities. As most of the image attributes often contains visual entities corresponding to any individual, hence, exploration and forging of such attributes with malicious intention often leads to social and personal life violation and also causes intellectual property right violation when social media, matrimonial and business applications are concerned. Although an extensive research effort endeavored pertaining to image forensics in the past, but existing techniques lack effectiveness towards maintaining equilibrium in between both image forensics and image quality assessment performances from computational viewpoint. Addressing this limitation associated with the existing system, this proposed study has come up with a novel solution which achieves higher degree of image forensics without compromising the visual perception of an image. The study formulates an intelligent empirical framework which determines cost-effective authentication of an image object from both complexity and quality viewpoint. Finally, the study also presented a numerical simulation outcome to ensure the performance efficiency of the system.

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Section: Introductionmentioning

confidence: 99%