KEYWORDS: Image encryption, Data hiding, Computer security, Image transmission, Image storage, Image restoration, Data transmission, Data storage, Information security, Clouds
Reversible information hiding in the encrypted domain has been utilized in numerous programs as it enables the embedding of secret information within the encrypted data while safeguarding the image content. Owing to its significance in the realm of privacy, this technology has garnered substantial attention and undergone significant development. Distinct from the traditional end-to-end reversible information hiding in the encrypted domain, the reversible information hiding scheme based on secret sharing is more applicable to applications in the cloud environment due to its multi-party security and fault tolerance. This paper undertakes a review of the development progress of Reversible information hiding based on secret sharing and conducts an analysis of the characteristics of existing schemes.
Deep learning technology has developed rapidly in recent years, and deep learning-based steganography and steganalysis techniques have also achieved fruitful results. In the past few years, the over-expanded structure of steganalyzers based on deep learning has led to huge computational and storage costs. In this article, we propose image steganalysis based on model compression, and apply the model compression method to image steganalysis to reduce the network infrastructure of the existing large-scale over-parameter steganalyzer based on deep learning. We conducted extensive experiments on the BOSSBase+BOWS2 dataset. As can be seen from the experiment, compared with the original steganalysis model, the model structure we proposed can achieve performance with fewer parameters and floating-point operations. This model has better portability and scalability.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.