Abstract
People share pictures freely with their loved ones and others using smartphones or social networking sites. The news industry and the court of law use the pictures as evidence for their investigation. Simultaneously, user-friendly photo editing tools alter the content of pictures and make their validity ques-tionable. Over two decades, research work is going on in image forensics to determine the picture’s trustworthiness. This paper proposes an efficient statistical method based on Block Artificial Grids in double compressed images to identify regions attacked by image manipulation. In contrast to existing approaches, the proposed approach extracts the artefacts on individual objects instead of the entire image. A localized algorithm is proposed based on the cosine dissimilarity between objects and exploit the tampered object with maximum dissimilarity among objects. The experimental results reveals that the proposed method is superior over other current methods.
Highlights
Now-a-days, people freely share their ideas, pictures, and comments on social networking sites
Unlike other techniques that produce probability maps from 8x8 discrete cosine transform (DCT) coefficients, we proposed an adequate statistical model that characterizes the fingerprints of block artificial grids (BAG) and works for any compression with any quality factor in the spatial domain
Both fully convolutional network (FCN)+region proposal network (RPN) and normalized grey level co-occurrence matrix (NGLCM) methods decreased their average F-measure as the Joint Photographic Experts Group (JPEG) compression quality factory is reduced towards 20
Summary
Now-a-days, people freely share their ideas, pictures, and comments on social networking sites. A manipulated image significantly impacts the trustworthiness when used for evidence [2] [3] It brings a significant challenge in image forensics to discover the original one from manipulated at the same time establish its authenticity and locate the tampered region [4]. Different tampering techniques in the literature assume that images taken from different camera models or different processing operations introduce inherent patterns into tampered image [6] [7][8] [9] It assumes that these underlying patterns consistent throughout the original image, and when any manipulation attacks it, there will be inconsistency in those patterns. Some postprocessing techniques will apply to the tampered region to make the attack invisible and difficult to trace to the human eye [12] This challenge attracted many researchers to find various techniques for detecting image splicing. When any splicing attack manipulates the image, it leads to discontinuities, and these statistical traces use to exploit tampering attacks, such as JPEG quantization artefacts and JPEG grid alignment discontinuities [17] [18]
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