High-dimensional statistical supervised learning for extracting information in steganography
| Autor: | Yejin Kim, Joongheon Kim, Miji Park, Kyeong Seon Kim, Soo Hyun Park, Sunjun Hwang, Junhui Kim |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
021110 strategic
defence & security studies Steganography business.industry Computer science Supervised learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Pattern recognition 02 engineering and technology Image (mathematics) 030507 speech-language pathology & audiology 03 medical and health sciences Distortion Key (cryptography) Artificial intelligence 0305 other medical science business Transform coding |
| Zdroj: | ICOIN |
| DOI: | 10.1109/icoin.2018.8343252 |
| Popis: | One of major research topics in digital steganography is the methods which can avoid information loss while the image files experience distortion. The image distortion may lead to the key value distortion, and eventually, it becomes very hard to get correct information from the distorted images. Therefore, this paper proposes a scheme which encodes and decodes the given images with high-dimensional regression-based supervised learning. With this proposed method, several original messages are embedded in a file; and high-dimensional regression-based supervised learning conducts for extracting exact one final message from the several original messages where every single original message is not same with the final message. In this case, if attackers read some messages from the image, it is hard to extract our final message. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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