Abstract
In this paper, we propose the use of non-negative matrix factorization (NMF) for 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 intentional attacks of guessing and forgery. Our work is motivated by the fact that standard-rank reduction techniques, such as QR and singular value decomposition, 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 constraints results in bases that capture local components 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 the design of detectors that are both computationally simple and, at the same time, optimal in the sense of minimizing error probabilities. Receiver operating characteristics analysis over a large image database reveals that the proposed algorithms significantly outperform existing approaches for image hashing.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 376-390 |
| Number of pages | 15 |
| Journal | IEEE Transactions on Information Forensics and Security |
| Volume | 2 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 1 2007 |
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All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality
- Computer Networks and Communications
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Robust and secure image hashing via non-negative matrix factorizations. / Monga, Vishal; Mihçak, M. Kivanç.
In: IEEE Transactions on Information Forensics and Security, Vol. 2, No. 3, 01.09.2007, p. 376-390.Research output: Contribution to journal › Article
TY - JOUR
T1 - Robust and secure image hashing via non-negative matrix factorizations
AU - Monga, Vishal
AU - Mihçak, M. Kivanç
PY - 2007/9/1
Y1 - 2007/9/1
N2 - In this paper, we propose the use of non-negative matrix factorization (NMF) for 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 intentional attacks of guessing and forgery. Our work is motivated by the fact that standard-rank reduction techniques, such as QR and singular value decomposition, 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 constraints results in bases that capture local components 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 the design of detectors that are both computationally simple and, at the same time, optimal in the sense of minimizing error probabilities. Receiver operating characteristics analysis over a large image database reveals that the proposed algorithms significantly outperform existing approaches for image hashing.
AB - In this paper, we propose the use of non-negative matrix factorization (NMF) for 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 intentional attacks of guessing and forgery. Our work is motivated by the fact that standard-rank reduction techniques, such as QR and singular value decomposition, 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 constraints results in bases that capture local components 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 the design of detectors that are both computationally simple and, at the same time, optimal in the sense of minimizing error probabilities. Receiver operating characteristics analysis over a large image database reveals that the proposed algorithms significantly outperform existing approaches for image hashing.
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UR - http://www.scopus.com/inward/citedby.url?scp=34548085689&partnerID=8YFLogxK
U2 - 10.1109/TIFS.2007.902670
DO - 10.1109/TIFS.2007.902670
M3 - Article
AN - SCOPUS:34548085689
VL - 2
SP - 376
EP - 390
JO - IEEE Transactions on Information Forensics and Security
JF - IEEE Transactions on Information Forensics and Security
SN - 1556-6013
IS - 3
ER -