Coverless Image Steganography Based on Optical Mark Recognition and Machine Learning
The most significant factor to consider during private information transmission through the internet (i.e., insecure channel) is security. So, to keep this data from unauthorized access during transmission, steganography is used. Steganography is the scheme of securing sensitive information by conce...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9319214/ |
| _version_ | 1819175735355506688 |
|---|---|
| author | Al Hussien S. Saad M. S. Mohamed Eslam H. Hafez |
| author_facet | Al Hussien S. Saad M. S. Mohamed Eslam H. Hafez |
| author_sort | Al Hussien S. Saad |
| collection | DOAJ |
| description | The most significant factor to consider during private information transmission through the internet (i.e., insecure channel) is security. So, to keep this data from unauthorized access during transmission, steganography is used. Steganography is the scheme of securing sensitive information by concealing it within carriers such as digital images, videos, audio, text, etc. Current image steganography methods work as follows; it assigns cover image then embeds the secret message within it by pixels' modifications, creating the resultant stego-image. These modifications allow steganalysis algorithms to detect the embedded secret message. So, a coverless data hiding concept is proposed to solve this problem. Coverless does not mean that the secret message will be transmitted without using a cover file, or the cover file can be discarded. Instead, the secret message will be embedded by generating a cover file or a secret message mapping. In this paper, a novel, highly robust coverless image steganography method based on optical mark recognition (OMR) and rule-based machine learning (RBML) is proposed. |
| first_indexed | 2024-12-22T20:59:35Z |
| format | Article |
| id | doaj.art-b10c647467604b9c9f6c7ad6c6ffce86 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:59:35Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b10c647467604b9c9f6c7ad6c6ffce862022-12-21T18:12:52ZengIEEEIEEE Access2169-35362021-01-019165221653110.1109/ACCESS.2021.30507379319214Coverless Image Steganography Based on Optical Mark Recognition and Machine LearningAl Hussien S. Saad0https://orcid.org/0000-0003-2232-8886M. S. Mohamed1Eslam H. Hafez2Computer Science Department, Faculty of Science, Minia University, Minya, EgyptDepartment of Mathematics, College of Science, Taif University, Taif, Saudi ArabiaMathematics Department, Faculty of Science, Helwan University, Helwan, EgyptThe most significant factor to consider during private information transmission through the internet (i.e., insecure channel) is security. So, to keep this data from unauthorized access during transmission, steganography is used. Steganography is the scheme of securing sensitive information by concealing it within carriers such as digital images, videos, audio, text, etc. Current image steganography methods work as follows; it assigns cover image then embeds the secret message within it by pixels' modifications, creating the resultant stego-image. These modifications allow steganalysis algorithms to detect the embedded secret message. So, a coverless data hiding concept is proposed to solve this problem. Coverless does not mean that the secret message will be transmitted without using a cover file, or the cover file can be discarded. Instead, the secret message will be embedded by generating a cover file or a secret message mapping. In this paper, a novel, highly robust coverless image steganography method based on optical mark recognition (OMR) and rule-based machine learning (RBML) is proposed.https://ieeexplore.ieee.org/document/9319214/Coverless information hidingoptical mark recognition (OMR)rule-based machine learning (RBML)image steganography |
| spellingShingle | Al Hussien S. Saad M. S. Mohamed Eslam H. Hafez Coverless Image Steganography Based on Optical Mark Recognition and Machine Learning IEEE Access Coverless information hiding optical mark recognition (OMR) rule-based machine learning (RBML) image steganography |
| title | Coverless Image Steganography Based on Optical Mark Recognition and Machine Learning |
| title_full | Coverless Image Steganography Based on Optical Mark Recognition and Machine Learning |
| title_fullStr | Coverless Image Steganography Based on Optical Mark Recognition and Machine Learning |
| title_full_unstemmed | Coverless Image Steganography Based on Optical Mark Recognition and Machine Learning |
| title_short | Coverless Image Steganography Based on Optical Mark Recognition and Machine Learning |
| title_sort | coverless image steganography based on optical mark recognition and machine learning |
| topic | Coverless information hiding optical mark recognition (OMR) rule-based machine learning (RBML) image steganography |
| url | https://ieeexplore.ieee.org/document/9319214/ |
| work_keys_str_mv | AT alhussienssaad coverlessimagesteganographybasedonopticalmarkrecognitionandmachinelearning AT msmohamed coverlessimagesteganographybasedonopticalmarkrecognitionandmachinelearning AT eslamhhafez coverlessimagesteganographybasedonopticalmarkrecognitionandmachinelearning |