• 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