<div>Steganalysis is the study of detecting secret information hidden in objects such as
images, videos, texts, time series and games via steganography. Among those objects,
the image is the most widely used object to hide secret messages. Detection of possible
secret information hidden in images has attracted a lot of attention over the past ten
years. People may conduct covert communications by exchanging images in which
secret messages may be embedded in bits. One of main advantages of steganography
over cryptography is that the former makes this communication insensible for human
beings. So statistical methods or tools are needed to help distinguish cover images
from stego images. <br></div><div><br></div><div>In this thesis, we start with a discussion of image steganography. Different kinds
of embedding schemes for hiding secret information in images are investigated. We
also propose a hiding scheme using a reference matrix to lower the distortion caused
by embedding. As a result, we obtain Peak Signal-to-Noise Ratios (PSNRs) of stego
images that are higher than those given by a Sudoku-based embedding scheme. Next,
we consider statistical steganalysis of images in two different frameworks. We first
study staganalysis in the framework of statistical hypothesis testing. That is, we
cast a cover/stego image detection problem as a hypothesis testing problem. For this
purpose, we employ different statistical models for cover images and simulate the
effects caused by secret information embedding operations on cover images. Then
the staganalysis can be characterized by a hypothesis testing problem in terms of
the embedding rate. Rao’s score statistic is used to help make a decision. The
main advantage of using Rao’s score test for this problem is that it eliminates an assumption used in the previous work where approximated log likelihood ratio (LR)
statistics were commonly employed for the hypothesis testing problems.<br></div><div><br></div><div>We also investigate steganalysis using the deep learning framework. Motivated
by neural network architectures applied in computer vision and other tasks, we propose a carefully designed a deep convolutional neural network architecture to classify the cover and stego images. We empirically show the proposed neural network
outperforms the state-of-the-art ensemble classifier using a rich model, and is also
comparable to other convolutional neural network architectures used for steganalysis.<br></div><div><br></div>The image databases used in the thesis are available on websites cited in the thesis. The stego images are generated from the image databases using source code from the website. <a href="http://dde.binghamton.edu/download/">http://dde.binghamton.edu/download/</a>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/9108260 |
| Date | 13 August 2019 |
| Creators | Min Huang (7036661) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Statistical_Steganalysis_of_Images/9108260 |
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