DocumentCode :
432792
Title :
Steganalysis of quantization index modulation data hiding
Author :
Sullivan, K. ; Bi, Z. ; Madhow, U. ; Chandrasekaran, S. ; Manjunath, B.S.
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA
Volume :
2
fYear :
2004
fDate :
24-27 Oct. 2004
Firstpage :
1165
Abstract :
Quantization index modulation (QIM) techniques have been gaining popularity in the data hiding community because of their robustness and information-theoretic optimally against a large class of attacks. In this paper, we consider detecting the presence of QIM hidden data, which is an important consideration when data hiding is used for covert communication, or steganography. For a given host distribution, we are able to quantify detectability compactly in terms of a parameter related to the robustness of the hiding scheme to attacks. Using detection theory we show that QIM quickly transitions from easily detectable to virtually undetectable as this parameter varies. We also obtain performance benchmarks for QIM hiding in images, indicating that a scheme designed to be robust to say a moderate degree of JPEG compression, should be easily detectable. While practical application of detection theory to images is difficult because of statistical variations across images, we employ supervised learning to show that standard QIM schemes for images are indeed quite easily detectable. However, it remains an open issue as to whether it is possible to devise QIM variants that are less vulnerable to steganalysis.
Keywords :
cryptography; data compression; data encapsulation; image processing; learning (artificial intelligence); modulation; quantisation (signal); JPEG compression; QIM technique; covert communication; data hiding community; detection theory; information-theory; quantization index modulation; standard QIM scheme; steganography; supervised learning; Additive noise; Bismuth; Data encapsulation; Image coding; Quantization; Robustness; Spread spectrum communication; Steganography; Supervised learning; Transform coding;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2004. ICIP '04. 2004 International Conference on
ISSN :
1522-4880
Print_ISBN :
0-7803-8554-3
Type :
conf
DOI :
10.1109/ICIP.2004.1419511
Filename :
1419511
Link To Document :
بازگشت