Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Sisheng Chen"'
Publikováno v:
IEEE Access, Vol 11, Pp 3494-3510 (2023)
Deep learning is regarded as a promising solution for reversible steganography. There is an accelerating trend of representing a reversible steo-system by monolithic neural networks, which bypass intermediate operations in traditional pipelines of re
Externí odkaz:
https://doaj.org/article/bfbd0bd400944a4b91b7e7610b025792
Publikováno v:
IEEE Access, Vol 9, Pp 116427-116439 (2021)
Trust and security are fundamental to the successful adoption of the Internet of Things (IoT). This paper proposes a secure message authentication scheme based on steganographic secret sharing for building trust in IoT systems. In our scheme, the mes
Externí odkaz:
https://doaj.org/article/c69db427ce9347188880383ffafa2ec3
Publikováno v:
IEEE Access, Vol 8, Pp 22345-22356 (2020)
This paper proposes a data hiding scheme for high quality stego-images in the encrypted images based on a homomorphic encryption algorithm and matrix embedding method. To achieve the message embedding in the encryption domain, we first present an ima
Externí odkaz:
https://doaj.org/article/55f66573731849f59e24f0cae616e51d
Autor:
Sisheng Chen, Chin-Chen Chang
Publikováno v:
IEEE Access, Vol 8, Pp 184199-184209 (2020)
This paper presents an improved secure reversible data hiding scheme in encrypted images based on integer transformation, which does not need using a data hider key to protect the embedded secret data. We first segment the original image into blocks
Externí odkaz:
https://doaj.org/article/a85ae5f4d70a47aabd6a33973490101c
Autor:
Li Xu, Sisheng Chen
Publikováno v:
International Journal of Distributed Sensor Networks, Vol 5, Iss 1, Pp 56-56 (2009)
Externí odkaz:
https://doaj.org/article/cd5c33873ed64621a3da7781bc53ecf2
Autor:
Sisheng Chen, Chin-Chen Chang
Publikováno v:
Multimedia Tools and Applications. 81:33397-33417
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eeef3597f9605edcc52d2b85541c2f56
https://doi.org/10.1007/s11042-022-13161-y
https://doi.org/10.1007/s11042-022-13161-y
Publikováno v:
Multimedia Tools and Applications. 80:33115-33138
Reversible data hiding in encrypted images (RDHEI) is a technique that allows for secret data to be embedded in images while preserving the privacy of the image content when the image owner and the data hider are different entities. This paper propos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::64807ba9da44bfaf401863e4e7a01713
https://doi.org/10.1007/s11042-021-11324-x
https://doi.org/10.1007/s11042-021-11324-x
Artificial neural networks have advanced the frontiers of reversible steganography. The core strength of neural networks is the ability to render accurate predictions for a bewildering variety of data. Residual modulation is recognised as the most ad
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::63816083210d572e4359e8a64704c91c
https://doi.org/10.21203/rs.3.rs-1740769/v1
https://doi.org/10.21203/rs.3.rs-1740769/v1
Publikováno v:
IEEE Access, Vol 8, Pp 22345-22356 (2020)
This paper proposes a data hiding scheme for high quality stego-images in the encrypted images based on a homomorphic encryption algorithm and matrix embedding method. To achieve the message embedding in the encryption domain, we first present an ima
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::146fc40255125ca51f749506a70bdd6d
https://ieeexplore.ieee.org/document/8966354/
https://ieeexplore.ieee.org/document/8966354/
Artificial neural networks have advanced the frontiers of reversible steganography. The core strength of neural networks is the ability to render accurate predictions for a bewildering variety of data. Residual modulation is recognised as the most ad
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::45cd37304ca6f1b92988724b5e3e4916
http://arxiv.org/abs/2202.02518
http://arxiv.org/abs/2202.02518