Subsequent embedding in targeted image steganalysis: Theoretical framework and practical applications
Steganalysis is a collection of techniques used to detect whether secret information is embedded in a carrier using steganography. Most of the existing steganalytic methods are based on machine learning, which typically requires training a classifier with laboratory data. However, applying machine-l...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/146673 |
| Acceso en línea: | http://hdl.handle.net/10609/146673 http://doi.org/10.1109/TDSC.2022.3154967 |
| Access Level: | acceso abierto |
| Palabra clave: | steganography steganalysis machine learning cover source mismatch stego source mismatch uncertainty esteganografia esteganàlisi aprenentatge automàtic coberta desajust de fonts desajust de fonts stego incertesa esteganografía esteganálisis aprendizaje automático cubrir desajuste de fuente desajuste de fuente stego incertidumbre |
| Sumario: | Steganalysis is a collection of techniques used to detect whether secret information is embedded in a carrier using steganography. Most of the existing steganalytic methods are based on machine learning, which typically requires training a classifier with laboratory data. However, applying machine-learning classification to a new source of data is challenging, since there is typically a mismatch between the training and the testing sets. In addition, other sources of uncertainty affect the steganlytic process, including the mismatch between the targeted and the true steganographic algorithms, unknown parameters such as the message length and even having a mixture of several algorithms and parameters, which would constitute a realistic scenario. This paper presents subsequent embedding as a valuable strategy that can be incorporated into modern steganalysis. Although this solution has been applied in previous works, a theoretical basis for this strategy was missing. Here, we cover this research gap by introducing the directionality property of features with respect to data embedding. Once this strategy is sustained by a consistent theoretical framework, new practical applications are also described and tested against standard steganography, moving steganalysis closer to real-world conditions. |
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