Perceptual image hashing maps an image to a fixed length binary string based on the image's appearance to the human eye, and has applications in image indexing, authentication, and watermarking. We present a general framework for perceptual image hashing using 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 enhance perceptual robustness further. 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.
['Vishal Monga', 'Brian L. Evans']
Robust perceptual image hashing using feature points