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- W3038284040 abstract "Conventional steganalysis detects the presence of steganography within single objects. In the real-world, we may face a complex scenario that one or some of multiple users, called actors, are guilty of using steganography, which is typically defined as the steganographer identification problem (SIP). We might use the conventional steganalysis algorithms to separate stego objects from cover objects and then identify the guilty actors. However, the guilty actors may be lost due to a number of false alarms. To deal with the SIP, most of the state-of-the-arts use unsupervised learning-based approaches. In their solutions, each actor holds multiple digital objects, from which a set of feature vectors can be extracted. The well-defined distances between these feature sets are determined to measure the similarity between the corresponding actors. By applying clustering or outlier detection the most suspicious actor(s) will be judged as the steganographer(s). Though the SIP needs further study, the existing works have good ability to identify the steganographer(s) when nonadaptive steganographic embedding was applied. In this chapter, we present foundational concepts and review advanced methodologies in SIP. It is self-contained and intended as a tutorial introducing the SIP in the context of media steganography." @default.
- W3038284040 created "2020-07-10" @default.
- W3038284040 creator A5049733139 @default.
- W3038284040 date "2020-01-01" @default.
- W3038284040 modified "2023-09-24" @default.
- W3038284040 title "Unsupervised steganographer identification via clustering and outlier detection" @default.
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- W3038284040 doi "https://doi.org/10.1016/b978-0-12-819438-6.00021-9" @default.
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