steganalysis and image quality measures
IJLTEMAS
VOLUME I ISSUE VII
2ICAE-2012 GOA
Steganalysis and Image Quality Measures
Neha Singh
Assoc. Prof., Department of Electronics and Communication Engineering
Institute of Engineering and Technology
Alwar, India
Abstract—Steganography is the art/ science of covert communication and steganalysis is the counter to it. Though the first goal of steganalysis is detection of hidden message, there can be additional goals such as disabling, extraction and
/or manipulating the original hidden message.
Detection of the secret message is enough to defeat the very purpose of steganography even if it is not extracted because detecting the existence of hidden data is enough if it needs to be destroyed.
The performance of steganalysis approach depends on identifying the appropriate Image
Quality Measures. A good objective quality measure should well reflect the distortion on the image due to embedding, blurring, noise and compression. A survey of Image Quality Measures used by the various reported Steganalysis techniques is presented in this paper.
KeywordsImage
Quality
Measures,
Steganography, Steganalysis, HVS based IQM,
Pixel based IQM.
I.
INTRODUCTION
Hiding information in digital content has a wide class of application and the techniques involved in such applications are collectively referred to as information hiding techniques [1]. Steganography refers to the art/science of embedding information in the digital images, aiming to convey messages secretly by concealing its very existence. Compared with the
Cryptography, modern steganography not only encrypts messages but also masks the very presence of the communication. A wide range of steganography techniques have been introduced which exploit spatial or transform domain characteristics of images to hide information. A survey of these techniques can be found in [2-7].
The message embedding into an image results in distortions which can be visible under human
VOLUME I ISSUE VII
2ICAE-2012 GOA
Steganalysis and Image Quality Measures
Neha Singh
Assoc. Prof., Department of Electronics and Communication Engineering
Institute of Engineering and Technology
Alwar, India
Abstract—Steganography is the art/ science of covert communication and steganalysis is the counter to it. Though the first goal of steganalysis is detection of hidden message, there can be additional goals such as disabling, extraction and
/or manipulating the original hidden message.
Detection of the secret message is enough to defeat the very purpose of steganography even if it is not extracted because detecting the existence of hidden data is enough if it needs to be destroyed.
The performance of steganalysis approach depends on identifying the appropriate Image
Quality Measures. A good objective quality measure should well reflect the distortion on the image due to embedding, blurring, noise and compression. A survey of Image Quality Measures used by the various reported Steganalysis techniques is presented in this paper.
KeywordsImage
Quality
Measures,
Steganography, Steganalysis, HVS based IQM,
Pixel based IQM.
I.
INTRODUCTION
Hiding information in digital content has a wide class of application and the techniques involved in such applications are collectively referred to as information hiding techniques [1]. Steganography refers to the art/science of embedding information in the digital images, aiming to convey messages secretly by concealing its very existence. Compared with the
Cryptography, modern steganography not only encrypts messages but also masks the very presence of the communication. A wide range of steganography techniques have been introduced which exploit spatial or transform domain characteristics of images to hide information. A survey of these techniques can be found in [2-7].
The message embedding into an image results in distortions which can be visible under human
References: EURASIP Journal on Applied Signal Processing, 2002:6, 613-621. Control, Automation, Robotics and Vision Conference, 2004. Trans.on Image Processing, Vol 12, No. 2, Feb, 2003. (2009); doi:10.1117/12.810507. Decomposition, Prediction-Error Image, and Neural Network‖ IEEE ICME, pp 269-272, 2005. Coding and Computing, Vol.1, 2005. Machines‖, Proc. Security, Steganography, and Watermarking of Multimedia Contents, 2004, pp.35-45. in Pattern and Image Analysis, 2005, pp. 1-10.