IEEE Trans. on Image Processing,
vol. 15, no. 11, pp. 3452-3465, Nov. 2006.
Perceptual Image Hashing Via Feature Points:
Performance Evaluation and Trade-Offs
Vishal Monga and
Brian L. Evans
Center for Perceptual Systems,
The University of Texas at Austin,
Austin, TX 78712 USA
Draft of Paper -
Image Hashing Research
at UT Austin
We propose an image hashing paradigm using visually significant
feature points. The feature points should be largely invariant under
perceptually insignificant distortions. To satisfy this, we propose
an iterative feature detector to extract significant geometry
preserving feature points. We apply probabilistic quantization on
the derived features to introduce randomness, which in turn reduces
vulnerability to adversarial attacks. The proposed hash algorithm
withstands standard benchmark (e.g. Stirmark) attacks including
compression, geometric distortions of scaling and small angle
rotation, and common signal processing operations. Content changing
(malicious) manipulations of image data are also accurately
detected. Detailed statistical analysis in the form of receiver
operating characteristic (ROC) curves is presented and reveals the
success of the proposed scheme in achieving perceptual robustness
while avoiding misclassification.
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Last Updated 11/01/06.