DocumentCode
2485701
Title
Breaking the YASS algorithm via pixel and DCT coefficients analysis
Author
Yu, Xiaoyi ; Babaguchi, Noboru
Author_Institution
Grad. Sch. of Eng., Osaka Univ., Suita
fYear
2008
fDate
8-11 Dec. 2008
Firstpage
1
Lastpage
4
Abstract
In this paper, we present a steganalytic method that can reliably detect messages hidden in JPEG images using the steganographic algorithm YASS, which is a JPEG steganographic method shown to be undetectable using current best blind steganalysis classifiers. The key element of the method is features extracted from the imagepsilas pixels and DCT coefficients. Although the YASS process effectively disables the calibration based and the noise model based JPEG steganalyzers, it also disturbs the pixels and DCT coefficients dependency after the secret message embedding. An SVM based classifier is trained based on the extracted features for the detection of the presence of steganography. The method is tested on a diverse set of test images that include both originally uncompressed and compressed images in the TIF and JPEG formats. Our experimental results have demonstrated that the proposed steganalyzers can reliably break YASS.
Keywords
data compression; discrete cosine transforms; feature extraction; image classification; image coding; steganography; support vector machines; DCT coefficient analysis; JPEG image; SVM based classifier training; YASS steganographic algorithm; blind steganalysis classifier; feature extraction; noise model; pixel analysis; secret message embedding; steganalytic method; Algorithm design and analysis; Calibration; Computer vision; Discrete cosine transforms; Feature extraction; Pixel; Steganography; Support vector machine classification; Support vector machines; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location
Tampa, FL
ISSN
1051-4651
Print_ISBN
978-1-4244-2174-9
Electronic_ISBN
1051-4651
Type
conf
DOI
10.1109/ICPR.2008.4761636
Filename
4761636
Link To Document