Robust perceptual image hashing via matrix invariants

Kozat, S.S.; Venkatesan, Ramarathnam; Mihcak, M.K.

doi:10.1109/icip.2004.1421855

Cited by 187 publications

(128 citation statements)

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“…It can be observed that there is no perfect scheme that satisfies all desirable requirements. A high robustness may couple with some other sacrifices, such as the high complexity in [12], [41], [42], low discrimination to CCOs [8], [10]. Accordingly, in some cases [8], [10], [46], the discrim- ination to CCOs seems to be conflicted with robustness.…”

Section: Discussionmentioning

confidence: 99%

“…Motivated by [41], Monga et al [12] propose to use Non-negative Matrix Factorization (NMF) for image hashing because of its non-negativity constraints. The NMF is also applied twice on the image, in conjunction with pseudorandomization to generate the secure hash sequence.…”

Section: Approaches Based On Dimension Reductionmentioning

confidence: 99%

“…Kozat et al [41] choose the Singular Value Decomposition (SVD) to generate the low-rank approximation due to some of its provable optimality properties. Intermediate features are firstly generated by applying SVD on the image.…”

Section: Approaches Based On Dimension Reductionmentioning

confidence: 99%

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A Survey on Image Hashing for Image Authentication

Rhee

2010

IEICE Trans. Inf. & Syst.

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SUMMARYThe traditional cryptographic hash functions are sensitive to even one-bit difference of the input message. While multimedia data always undergo compression or other signal processing operations, which lead to the unsuitability of multimedia authentication using cryptographic hash. The image hashing has emerged recently which captures visual essentials for robust image authentication. In this paper, we give a comprehensive survey of image hashing. We present an overview of various image hashing schemes and discuss their advantages and limitations in terms of security, robustness, and discrimination under different types of operations on the image.

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Section: Discussionmentioning

confidence: 99%

Section: Approaches Based On Dimension Reductionmentioning

confidence: 99%

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A Survey on Image Hashing for Image Authentication

Rhee

2010

IEICE Trans. Inf. & Syst.

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“…• Coarse-representation-based schemes (Fridrich & Goljan, 2000;Kozat et al, 2004;Mihçak & R.Venkatesan, 2001;Swaminathan et al, 2006): In this category of methods, the perceptual hashes are calculated by making use of coarse information of the whole image, such as the spatial distribution of significant wavelet coefficients, the low-frequency coefficients of Fourier transform, and so on.…”

Section: Perceptual Image Hashing Methods Classificationmentioning

confidence: 99%

Perceptual Image Hashing

Hadmi¹,

Puech²,

Said³

et al. 2012

Watermarking - Volume 2

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“…In [11], the feature extraction consists of a random tiling of the DWT subbands of the image; the mean (or variance) of the pixel values in each random rectangle are used to form the feature vector, which is randomly quantized and compressed to generate the hash. The authors of [12] compute SVD from random blocks in the image, and use the obtained intermediate features to produce a robust and secure image hash. Swaminathan et.…”

Section: A Related Workmentioning

confidence: 99%

Hash-Based Identification of Sparse Image Tampering

Tagliasacchi

Valenzise

Tubaro

2009

IEEE Trans. on Image Process.

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In the last decade, the increased possibility to produce, edit and disseminate multimedia contents has not been adequately balanced by similar advances in protecting these contents from unauthorized diffusion of forged copies. When the goal is to detect whether or not a digital content has been tampered with in order to alter its semantics, the use of multimedia hashes turns out to be an effective solution to offer proof of legitimacy and to eventually identify the introduced tampering.We propose an image hashing algorithm based on compressive sensing principles, which solves both the authentication and the tampering identification problems. The original content producer generates a hash using a small bit budget by quantizing a limited number of random projections of the authentic image. The content user receives the (possibly altered) image, and uses the hash to estimate the mean square error distortion between the original and the received image. In addition, if the introduced tampering is sparse in some orthonormal basis or redundant dictionary, an approximation is given in the pixel domain. We emphasize that the hash is universal, e.g. the same hash signature can be used to detect and identify different types of tampering. At the cost of additional complexity at the decoder, the proposed algorithm is robust to moderate content-preserving transformations including cropping, scaling and rotation. In addition, in order to keep the size of the hash small, hash encoding/decoding takes advantage of distributed source codes.

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Robust perceptual image hashing via matrix invariants

Cited by 187 publications

References 3 publications

A Survey on Image Hashing for Image Authentication

A Survey on Image Hashing for Image Authentication

Perceptual Image Hashing

Hash-Based Identification of Sparse Image Tampering

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