Abstract
The proliferation of easy multimedia editing tools has ruined the trust in what we see. Forensic techniques are proposed to detect forgeries unnoticeable by naked human eyes. In this paper, we focus on a specific copy-move forgery attack that happens to alter portions within an image. It may be aimed to hide any sensitive information contained in a particular image portion or misguide the facts. Here, we propose to exploit the image’s statistical properties, specifically, mean and variance, to detect the forged portions. A block-wise comparison is made based on these properties to localize the forged region called a prediction mask. Post-processing methods have been proposed to reduce false positives and improve the accuracy(F-score) of the prediction mask. This decrease in FPR in the final result is comes from post processing method of overlaying multiple masks with different values of threshold and block_size of the sliding window.
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Nirwal, A., Khandelwal, R., Patel, S., Singh, P. (2020). A Forensic Technique to Detect Copy-Move Forgery Based on Image Statistics. In: Batina, L., Picek, S., Mondal, M. (eds) Security, Privacy, and Applied Cryptography Engineering. SPACE 2020. Lecture Notes in Computer Science(), vol 12586. Springer, Cham. https://doi.org/10.1007/978-3-030-66626-2_5
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DOI: https://doi.org/10.1007/978-3-030-66626-2_5
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