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• W4241160147 abstract "This article describes how digital forensic techniques for source investigation and identification enable forensic analysts to map an image under question to its source device, in a completely blind way, with no a-priori information about the storage and processing. Such techniques operate based on blind image fingerprinting or machine learning based modelling using appropriate image features. Although researchers till date have succeeded to achieve extremely high accuracy, more than 99% with 10-12 candidate cameras, as far as source device prediction is concerned, the practical application of the existing techniques is still doubtful. This is due to the existence of some critical open challenges in this domain, such as exact device linking, open-set challenge, classifier overfitting and counter forensics. In this article, the authors identify those open challenges, with an insight into possible solution strategies." @default.
• W4241160147 created "2022-05-12" @default.
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• W4241160147 date "2020-01-01" @default.
• W4241160147 modified "2023-09-27" @default.
• W4241160147 title "Blind Image Source Device Identification" @default.
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• W4241160147 doi "https://doi.org/10.4018/978-1-7998-2466-4.ch034" @default.
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