Technical Program

Paper Detail

Paper:	IMDSP-L10.3
Session:	Watermarking
Time:	Friday, May 19, 17:10 - 17:30
Presentation:	Lecture
Topic:	Image and Multidimensional Signal Processing: Authentication and Watermarking
Title:	ROBUST IMAGE HASHING VIA NON-NEGATIVE MATRIX FACTORIZATIONS
Authors:	Vishal Monga, Xerox Innovation Group, United States; Kivanc Mihcak, Microsoft Research, United States
Abstract:	In this paper, we propose the use of non-negative matrix factorization (NMF) for robust image hashing. In particular, we view images as matrices and the goal of hashing as a randomized dimensionality reduction that retains the essence of the original image matrix while preventing against intentional attacks of guessing and forgery. Our work is motivated by the fact that standard-rank reduction techniques such as the QR, and Singular Value Decompo- sition (SVD), produce low rank bases which do not respect the structure (i.e. non-negativity for images) of the original data. We observe that NMFs have two very desirable properties for secure image hashing applications: 1.) The additivity property resulting from the non-negativity con- straints results in bases that capture local characteristics of the image, thereby significantly reducing misclassification, and 2.) the effect of geometric attacks on images in the spatial domain manifests (approximately) as independent identically distributed noise on NMF vectors, allowing design of detectors that are both computationally simple and at the same time optimal in the sense of minimizing error prob- abilities. ROC (receiver operating characteristics) analysis over a large image database reveals that the proposed algorithms significantly outperform existing approaches for robust image hashing.