• Steven K. Tjoa and K. J. Ray Liu
• IEEE Int. Conf. Acoustics, Speech, and Signal Processing, March 2010
• Download: Paper, Poster, BibTeX

  @INPROCEEDINGS{tjoa2010icassp_cooccurrence,
    title = "Multiplicative Update Rules for Nonnegative Matrix Factorization with Co-occurrence Constraints",
    author = "Steven K. Tjoa and K. J. Ray Liu",
    booktitle = "Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing",
    address = "Dallas, TX",
    year = "2010",
    month = mar,
    pages = "449--452"
  };

Nonnegative matrix factorization (NMF) has become a popular tool for discovering structure in a variety of signals. When applied to a musical audio signal, NMF builds a set of dictionary atoms that represent the individual musical sources in the signal. To perform music transcription, we map the learned dictionary atoms to musical notes and beats.

The basic formulation of NMF has the notable disadvantage that sources may require more than a single dictionary atom in order to be approximated accurately. For example, one note played by a violin may require multiple dictionary atoms to be accurately represented due to the vibrato induced by the performer. In the presence of many other musical sources, the correspondence between atoms becomes unclear.

In this paper, we introduce three new update rules to enforce dependence among dictionary atoms by incorporating co-occurrence constraints into NMF. These co-occurrence constraints have shown to be useful for describing sources with multiple, co-occurring dictionary atoms by grouping similar atoms into sets. The proposed rules are conceptually simple, easy to implement, and effective for describing sources using multiple dictionary atoms.

• Steven K. Tjoa, Matthew C. Stamm, W. Sabrina Lin, and K. J. Ray Liu
• IEEE Int. Conf. Acoustics, Speech, and Signal Processing, March 2010
• Download: Paper, Poster, BibTeX

  @INPROCEEDINGS{tjoa2010icassp_harmonic,
    title = "Harmonic Variable-Size Dictionary Learning for Music Source Separation",
    author = "Steven K. Tjoa and Matthew C. Stamm and W. Sabrina Lin and K. J. Ray Liu",
    booktitle = "Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing",
    address = "Dallas, TX",
    year = "2010",
    month = mar,
    pages = "413--416"
  };

Methods that employ dictionary learning and sparse coding have become popular for discovering structure in acoustic signals. Unfortunately, these methods also share a common limitation. When there is significant spectral-temporal overlap among the dictionary atoms present in a signal, it becomes difficult for these methods to learn atoms properly. Often, information from multiple atoms is represented as a single atom by the learning procedure. If an atom in the output dictionary contains musical information from multiple sources, transcription and source separation cannot be accurately performed.

In this paper, we propose a novel dictionary learning method that performs well despite the presence of spectral-temporal overlap among dictionary atoms. Our method imposes a harmonic constraint that restricts each atom to represent at most one pitch. Furthermore, our method is flexible by allowing the size of the dictionary to grow based upon the complexity of the input signal. Our method consistently achieves higher recall and precision than other well-known dictionary learning algorithms.

• Matthew C. Stamm, Steven K. Tjoa, W. Sabrina Lin, and K. J. Ray Liu
• IEEE Int. Conf. Acoustics, Speech, and Signal Processing, March 2010
• Download: Paper, BibTeX

  @INPROCEEDINGS{stamm2010icassp_antiforensics,
    title = "Anti-Forensics of {JPEG} Compression",
    author = "Matthew C. Stamm and Steven K. Tjoa and W. Sabrina Lin and K. J. Ray Liu",
    booktitle = "Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing",
    address = "Dallas, TX",
    year = "2010",
    month = mar,
    pages = "1694--1697"
  };

Digital image forensics is a relatively new field that has only become popular within the last seven years. A newer body of research involves anti-forensics — methods of processing images that disguise evidence of earlier image processing or tampering. By altering the forensically significant properties in an image, existing image forensic systems may be rendered useless. Like many areas of security, we benefit from the study of anti-forensics by understanding the attacks that are most likely to occur before they actually do. Then, we can refine our image forensic systems further.

Earlier, our group proposed the first forensic system that identifies which compression method was used upon a digital image. In this paper, we propose an anti-forensic method for defeating the identification of JPEG compression in an image. Our method alters the intrinsic fingerprint of JPEG-compressed images in the transform domain such that the attacked image is perceptually similar to the image before attack. To our knowledge, this work is the first to propose a method specifically designed to defeat compression identification systems, and we anticipate that more work will follow.

• 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.