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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Acknowledgement
This work was partly supported by National Natural Science Foundation of China (61772144, 61672008), Innovation Team Project (Natural Science) of the Education Department of Guangdong Province (2017KCXTD021), Foundation for Youth Innovation Talents in Higher Education of Guangdong Province (2018KQNCX139), Innovation Research Project (Natural Science) of Education Department of Guangdong Province (2016KTSCX077), Project for Distinctive Innovation of Ordinary Universities of Guangdong Province (2018KTSCX120), and Foreign Science and Technology Cooperation Plan Project of Guangzhou Science Technology and Innovation Commission (201807010059).
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Xie, G., Ren, J., Zhao, H., Zhao, S., Marshall, S. (2020). Evaluation of Deep Learning and Conventional Approaches for Image Steganalysis. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_33
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DOI: https://doi.org/10.1007/978-3-030-39431-8_33
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