Automatic Recovery of Hidden Image from Image Steganography Using DNN and Local Entropy Features
- Authors
- Lee, Jae Hoon; Kang, D.Y.; Lee, J.E.; Lee, Sang-Hwa; Park, Jong-Il
- Issue Date
- Jul-2020
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Data hiding; Deep neural network; Image entropy; Image steganography; Steganalysis
- Citation
- ITC-CSCC 2020 - 35th International Technical Conference on Circuits/Systems, Computers and Communications, pp.440 - 445
- Indexed
- SCOPUS
- Journal Title
- ITC-CSCC 2020 - 35th International Technical Conference on Circuits/Systems, Computers and Communications
- Start Page
- 440
- End Page
- 445
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145402
- Abstract
- Image steganography hides secret information in an image called cover image so naturally that the other users can not recognize the existence of information in the revealed image. This paper deals with an approach to recover the hidden image information from image steganography. The proposed approach investigates that the decoded hidden image information is a normal image or not. The normal and incorrectly decoded abnormal images have been trained using a deep neural network model and entropy features. The discrimination is processed with image patches since the information may be partially embedded in the cover image. The experiments are performed with respect to the various data capacities. The proposed approach discriminates and recovers the hidden image information automatically from a tremendously large number of steganography encoding methods.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 컴퓨터소프트웨어학부 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.