• W. Sabrina Lin, Steven K. Tjoa, H. Vicky Zhao, and K. J. Ray Liu
• IEEE Trans. Information Forensics and Security, September 2009
• Download: Paper, BibTeX

  @ARTICLE{lin2009tifs,
    title = "Digital Image Source Coder Forensics via Intrinsic Fingerprints",
    author = "W. Sabrina Lin and Steven K. Tjoa and H. Vicky Zhao and K. J. Ray Liu",
    journal = "IEEE Trans. Information Forensics and Security",
    year = "2009",
    month = sep,
    volume = "4",
    number = "3",
    pages = "460--475"
  };

Multimedia forensic methods allow us to verify and maintain the integrity of our multimedia data. For example, we can embed a watermark into a digital image to bind the identity of its owner to the image itself. However, traditional forensic approaches such as watermarking are not applicable in many real-world scenarios, for example, when we do not have access to the original data.

In this paper, we investigate the use of intrinsic fingerprints — subsets of data which are, or have become, an intrinsic part of the data in question — for formulating a forensic methodology that can identify the compression history of a digital image. By examining the intrinsic fingerprints in an image, we can tell what compression method (e.g., JPEG, etc.) was used in order to determine the origin of the image and thereby assess its authenticity.

Although there exist methods that estimate parameters of individual compression schemes, to our knowledge, our work is the first to address the problem of image compression identification and offer a unified forensic framework incorporating multiple encoders.

• W. Sabrina Lin, Steven Tjoa, H. Vicky Zhao, and K. J. Ray Liu
• IEEE Int. Conf. Multimedia and Expo, July 2007
• Download: Paper, BibTeX

  @INPROCEEDINGS{lin2007icme,
    title = "Image Source Coding Forensics via Intrinsic Fingerprints",
    author = "W. Sabrina Lin and Steven Tjoa and H. Vicky Zhao and K. J. Ray Liu",
    booktitle = "Proc. IEEE Int. Conf. Multimedia and Expo",
    address = "Beijing, China",
    year = "2007",
    month = jul,
    pages = "1127--1130",
  };

The most popular image compression schemes in use today are lossy, i.e., compression imposes some irreversible distortion in the image in order to achieve a smaller file size. Because each compression method imposes different kinds of distortion, the distortion can act as a fingerprint of the compression method. Existing image compression methods can be grouped into a few categories — e.g., transform coding, vector quantization, subband coding, linear predictive coding, embedded coding — where all methods in the same category leave behind the same type of fingerprint in an image during compression.

In this paper, we analyze the intrinsic fingerprints of different types of image compression methods and propose a forensic system that identifies the type of the compression and provides a confidence measure in the system’s decision. Results show that the system can achieve a probability of detection of 0.82 for an image PSNR of 40 dB and even higher accuracy for lower PSNRs.

• Steven Tjoa, W. Sabrina Lin, H. Vicky Zhao, and K. J. Ray Liu
• IEEE Int. Conf. Acoustics, Speech, and Signal Processing, April 2007
• Download: Paper, Presentation, BibTeX

  @INPROCEEDINGS{tjoa2007icassp,
    title = "Block Size Forensic Analysis in Digital Images",
    author = "Steven Tjoa and W. Sabrina Lin and H. Vicky Zhao and K. J. Ray Liu",
    booktitle = "Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing",
    year = "2007",
    address = "Honolulu, HI",
    month = apr,
    volume = "1",
    pages = {I-633--I-636}
  };

Our work on image compression forensics attempts to identify the compression method used upon an image by analyzing its intrinsic fingerprint. Many popular image compression methods such as JPEG employ block processing. Therefore, to even begin forensic analysis for digital images, we must first address the presence of block processing on our image data. For forensic analysis of block-based coding schemes, estimating the block size is an obvious and crucial first step, because inaccurate block size estimation can possibly invalidate subsequent forensic tests.

Block artifact measurement is a well-established research area, with purposes primarily related to image restoration and distortion measurement. However, artifact measurement for the purpose of forensic analysis has not been explored. Here, we pose the following question: given a compressed image, can we detect the presence of block processing? If so, can we estimate the block size? Existing work in block artifact measurement is not tailored to answer this question due to strong assumptions placed upon the input data.

In this paper, we propose a novel scheme to detect the presence of block processing along with estimation of the block size. We first obtain a block artifact signature and then estimate the block size through maximum-likelihood estimation. Finally, we propose a binary hypothesis test that identifies the presence or absence of block processing with high detection rates and low false alarm rates.