Image Hashing Toobox in Matlab

Vishal Monga, Divyanshu Vats, and Brian L. Evans
The University of Texas at Austin, Austin, TX 78712 USA

Download Version 0.1 beta

Image Hashing Research at UT Austin



Hash functions are frequently called message digest functions. Their purpose is to extract a fixed-length bit string from a message (image, documents, etc.). Hash functions have found varied applications in various cryptographic, compiler and database search applications. In cryptography, hash functions are typically used for digital signatures to authenticate the message being sent so that the recipient can verify its source. Recently, there has been a lot of interest in using hash functions in multimedia applications both for security and indexing. A key feature of conventional cryptographic hashing algorithms such as message digest 5 (MD5) and secure hash algorithm 1 (SHA-1) is that they are extremely sensitive to the message, i.e. changing even one bit of the input will change the output dramatically. However, multimedia data such as digital images go through various manipulations such as compression, enhancement, cropping, and scaling. An image hash function should instead take into account the changes in the visual domain and produce hash values based on the image's visual appearance. Such a hash function would be useful in identifying images in databases, in which the image possibly undergoes incidental changes (such as compression and format changes, common signal processing operations, scanning or watermarking). A second significant application of a perceptual image hash could be for robust image authentication. In such cases, the hash must be invariant under perceptually insignificant modifications to the image but detect malicious tampering of image data. Several other applications can be identified in the areas of watermarking and information embedding in images.

Toolbox Demonstration

The hashingdemo performs an automated test for a perceptual match of two images. This is done by computing hashes from the two images and comparing the hash vectors for a meaningful notion of distance. Image hashing methods included in this demo are:
  1. Discrete Wavelet Transform (DWT) Based Image Hash [Venkatesan et al., 2000]
  2. Hashing Via Singular Value Decomposition (SVD) [Kozat et al., 2004]
  3. Feature Point Based Image Hashing [Monga & Evans, 2004-06]
Start the demo by typing hashingdemo from the MATLAB command prompt.

Example images that have been tampered with are available in the DemoImages sub-directory.

The image hashing toolbox requires Matlab 6.5 or higher, as well as the Image Processing Toolbox and Wavelet Toolbox in MATLAB.


Versions 0.1 of the toolbox was generated by Dr. Vishal Monga and Mr. Divyanshu Vats at UT Austin under the direction of Prof. Brian L. Evans.

Dr. Vishal Monga is currently a Member of Research Staff at Xerox Research Labs in El Segundo, California. Mr. Divyanshu Vats is currently a graduate student in electrical and computer engineering at Carnegie Mellon University in Pittsburg, Pennsylvania. Prof. Brian L. Evans is currently Professor of Electrical and Computer Engineering at The University of Texas at Austin in Austin, Texas.

For questions on the toolbox please write to Dr. Vishal Monga at