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Linear Operator
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Robust perceptual image hashing via matrix invariants
Robust perceptual image hashing via matrix invariants,10.1109/ICIP.2004.1421855,Suleyman S. Kozat,Ramarathnam Venkatesan,Mehmet Kivanç Mihçak
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Robust perceptual image hashing via matrix invariants
(
Citations: 47
)
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Suleyman S. Kozat
,
Ramarathnam Venkatesan
,
Mehmet Kivanç Mihçak
In this paper we suggest viewing images (as well as attacks on them) as a sequence of linear operators and propose novel hashing algorithms employing transforms that are based on matrix invari ants. To derive this sequence, we simply cover a two dimensional representation of an image by a sequence of (possibly overlapping) rectangles whose sizes and locations are chosen randomly1 from a suitable distribution. The restriction of the image (repre sentation) to each gives rise to a matrix . The fact that 's will overlap and are random, makes the sequence (respectively) a redundant and nonstandard representation of images, but is cru cial for our purposes. Our algorithms rst construct a secondary image, derived from input image by pseudorandomly extracting features that approx imately capture semiglobal geometric characteristics. From the secondary image (which does not perceptually resemble the input), we further extract the nal features which can be used as a hash value (and can be further suitably quantized). In this paper, we use spectral matrix invariants as embodied by
Singular Value
Decom position. Surprisingly, formation of the secondary image turns out be quite important since it not only introduces further robustness (i.e, resistance against standard
signal processing
transformations), but also enhances the
security properties
(i.e. resistance against in tentional attacks). Indeed, our experiments reveal that our hashing algorithms extract most of the geometric information from the im ages and hence are robust to severe perturbations (e.g. up to %50 cropping by area with 20 degree rotations) on images while avoid ing misclassication. Our methods are general enough to yield a watermark embedding scheme, which will be studied in another paper. as the seed of a secure PR number generator in the algorithms) due to the randomness introduced in the signal representation. In this paper, we apply our approach to the problem of image hashing and present several promising results. Moreover, we believe that our approach (or possibly a variant of it) can be extended to ad dress hashing and mark embedding problems for video and audio as well, with little or no straightforward modications.
Conference:
International Conference on Image Processing  ICIP
, vol. 5, pp. 34433446, 2004
DOI:
10.1109/ICIP.2004.1421855
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Citation Context
(38)
...The methods of feature extraction may be based on various transformations such as discrete wavelet transform (DWT) [8, 16], discrete cosine transform (DCT) [1, 6, 11], Radon transform [4] and FourierMellin transform [12], and on standardrank reduction techniques such as singular value decomposition (SVD) [
3
] and nonnegative matrix factorization (NMF) [9, 13]...
...In [
3
], Kozat et al. view attacks on the image as a sequence of linear operators, and propose a hashing algorithm using lowrank matrix approximations obtained by SVD...
Zhenjun Tang
,
et al.
Lexicographical framework for image hashing with implementation based ...
...The use of SVD decomposition in the image hashing problem was proposed by Kozat et all [
5
] where SVD decomposition is used twice and has been shown to be robust to some small variations in rotation and scaling...
...We compare the algorithm with and without image normalization [
5
]...
Ricardo Antoio Parrao Hernandez
,
et al.
Robust image hashing using image normalization and SVD decomposition
...
Kozat et al. (2004)
developed another hash by applying singular value decomposition to each image rectangle...
Lina Wang
,
et al.
Image authentication based on perceptual hash using Gabor filters
...Other works are in the same category such as [13], [5], [
14
]...
Azhar Hadmi
,
et al.
Statistical analysis of the quantization stage of robust perceptual im...
...In [
2
], subimages were pseudorandomly selected from the input image to compute the reduced Singular Value Decomposition (SVD)...
Rui Sun
,
et al.
Robust image hashing using locally linear embedding
References
(4)
Robust Image Hashing
(
Citations: 173
)
Ramarathnam Venkatesan
,
S.M. Koon
,
Mariusz H. Jakubowski
,
Pierre Moulin
Conference:
International Conference on Image Processing  ICIP
, vol. 3, pp. 664666 vol.3, 2000
Robust Hash Functions for Digital Watermarking
(
Citations: 107
)
Jiri Fridrich
,
Miroslav Goljan
Conference:
International Symposium on Information Technology  ITCC
, pp. 178183, 2000
A Perceptual Audio Hashing Algorithm: A Tool for Robust Audio Identification and Information Hiding
(
Citations: 42
)
Mehmet Kivanç Mihçak
,
Ramarathnam Venkatesan
Conference:
Information Hiding
, pp. 5165, 2001
Watermarking via Optimization Algorithms for Quantizing Randomized Statistics of Image Regions
(
Citations: 11
)
M. Kivanc Mihcak
,
Ramarathnam Venkatesan
,
Mustafa Kesal
Published in 2002.
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Citations
(47)
Lexicographical framework for image hashing with implementation based on DCT and NMF
Zhenjun Tang
,
Shuozhong Wang
,
Xinpeng Zhang
,
Weimin Wei
,
Yan Zhao
Journal:
Multimedia Tools and Applications  MTA
, vol. 52, no. 23, pp. 325345, 2011
Robust image hashing using image normalization and SVD decomposition
Ricardo Antoio Parrao Hernandez
,
Mariko Nakano Miyatake
,
Brian M. Kurkoski
Conference:
Midwest Symposium on Circuits and Systems  MWSCAS
, pp. 14, 2011
Image authentication based on perceptual hash using Gabor filters
Lina Wang
,
Xiaqiu Jiang
,
Shiguo Lian
,
Donghui Hu
,
Dengpan Ye
Journal:
Soft Computing  SOCO
, vol. 15, no. 3, pp. 493504, 2011
Statistical analysis of the quantization stage of robust perceptual image hashing
Azhar Hadmi
,
William Puech
,
Brahim Ait Es Said
,
Abdellah Ait Ouahman
Conference:
European Workshop on Visual Information Processing  EUVIP
, 2011
Robust image hashing using locally linear embedding
Rui Sun
,
Xiaoxing Yan
,
Zhizhong Ding
Conference:
International Conference on Computer Science and Service System  CSSS
, 2011