IMAGE FORGERY DETECTION USING CONVOLUTIONAL NEURAL NETWORK ALGORITHM
DOI:
https://doi.org/10.29121/shodhkosh.v5.i5.2024.2712Keywords:
Deep Learning, Forgery Detection, Image Features, Multimedia, Forensic AnalysisAbstract [English]
The validity of photos is being called into doubt because to the availability of strong image modification tools. This is particularly problematic where images hold significant influence, such as in legal proceedings, news articles, or insurance claims. The rapid advances in science and technology have made it easier than ever to access a wealth of knowledge through a range of multimedia platforms. However, because it is so easy to alter the contents using a variety of editing programs, the authenticity and integrity of multimedia content are in jeopardy. Forensics technology is being developed to address this issue. We focus on blind image forensics tools for copy-move forgeries in this survey. Copy-move forgeries are among the most used methods for manipulating images. They usually entail adding objects to the same image or covering them with flat regions. Image forensic techniques use a variety of complex procedures that have been documented in the literature to ascertain the integrity of a photograph. To conceal particular objects or provide the appearance of a duplicate, a section of an image is copied, then pasted back onto the original. One study concentrates on a specific type of image faking. Next, build the architecture for a convolutional neural network to determine whether or not the image is fake.
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Copyright (c) 2024 Dr.Gowsic K, Vinayaka Moorthi M, Siranjeevi S, Viswa G
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