Title
Second Level Steganalysis - Embeding Location Detection Using Machine Learning
Abstract
In recent years, various cloud-based services have been introduced in our daily lives, and information security is now an important topic for protecting the users. In the literature, many technologies have been proposed and incorporated into different services. Data hiding or steganography is a data protection technology, and images are often used as the cover data. On the other hand, steganalysis is an important tool to test the security strength of a steganography technique. So far, steganalysis has been used mainly for detecting the existence of secret data given an image, i.e., to classify if the given image is a normal or a stego image. In this paper, we investigate the possibility of identifying the locations of the embedded data if the a given image is suspected to be a stego image. The purpose is of two folds. First, we would like to confirm the decision made by the first level steganalysis; and the second is to provide a way to guess the size of the embedded data. Our experimental results show that in most cases the embedding positions can be detected. This result can be useful for developing more secure steganography technologies.
Year
DOI
Venue
2019
10.1109/ICAwST.2019.8923205
2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)
Keywords
Field
DocType
Steganography,Steganalysis,Machine Learning
Data mining,Steganography,Location detection,Embedding,Computer science,Information hiding,Information security,Steganalysis,Data Protection Act 1998,Cloud computing
Conference
ISSN
ISBN
Citations 
2325-5986
978-1-7281-3822-0
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Takumi Saito100.34
Qiangfu Zhao221462.36
Hiroshi Naito300.34