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Pixels-off: Data-augmentation Complementary Solution for Deep-learning Steganalysis

Abstract : After 2015, CNN-based steganalysis approaches have started replacing the two-step machine-learning-based steganalysis approaches (feature extraction and classification), mainly due to the fact that they offer better performance. In many instances, the performance of these networks depend on the size of the learning database. Until a certain point, the larger the database, the better the results. However, working with a large database with controlled acquisition conditions is usually rare or unrealistic in an operational context. An easy and efficient approach is thus to augment the database, in order to increase its size, and therefore to improve the efficiency of the steganalysis process. In this article, we propose a new way to enrich a database in order to improve the CNN-based steganalysis performance. We have named our technique "pixels-off". This approach is efficient, generic, and is usable in conjunction with other data-enrichment approaches. Additionally, it can be used to build an informed database that we have named "Side-Channel-Aware databases" (SCA-databases).
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Contributor : Marc Chaumont <>
Submitted on : Thursday, April 30, 2020 - 5:49:44 PM
Last modification on : Sunday, June 13, 2021 - 10:08:02 AM

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  • HAL Id : lirmm-02559838, version 1

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Mehdi Yedroudj, Marc Chaumont, Frédéric Comby, Ahmed Oulad-Amara, Patrick Bas. Pixels-off: Data-augmentation Complementary Solution for Deep-learning Steganalysis. Information Hiding and Multimedia Security, Jun 2020, Denver, United States. ⟨lirmm-02559838⟩

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