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Erschienen in:
Improving Image Caption Performance with Linguistic Context Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Evaluation of Deep Learning and Conventional Approaches for Image Steganalysis

verfasst von : Guoliang Xie, Jinchang Ren, Huimin Zhao, Sophia Zhao, Stephen Marshall

Erschienen in: Advances in Brain Inspired Cognitive Systems

Verlag: Springer International Publishing

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Abstract

Steganography is the technique that’s used to embed secret messages into digital media without changing their appearances. As a countermeasure to steganography, steganalysis detects the presence of hidden data in a digital content. For the last decade, the majority of image steganalysis approaches can be formed by two stages. The first stage is to extract effective features from the image content and the second is to train a classifier in machine learning by using the features from stage one. Ultimately the image steganalysis becomes a binary classification problem. Since Deep Learning related architecture unify these two stages and save researchers lots of time designing hand-crafted features, the design of a CNN-based steganalyzer has therefore received increasing attention over the past few years. In this paper, we will examine the development in image steganalysis, both in spatial domain and in JPEG domain, and discuss the future directions.

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Vorheriges Kapitel Blind Detection Based on Color SIFT
Nächstes Kapitel The Design Patent Images Classification Based on Image Caption Model
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Metadaten
Titel
Evaluation of Deep Learning and Conventional Approaches for Image Steganalysis
verfasst von
Guoliang Xie
Jinchang Ren
Huimin Zhao
Sophia Zhao
Stephen Marshall
Copyright-Jahr
2020
Buch
Advances in Brain Inspired Cognitive Systems

Print ISBN: 978-3-030-39430-1

Electronic ISBN: 978-3-030-39431-8

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-39431-8_33