New Iterative Geometric Methods for Robust Perceptual Image Hashing

Mihcak, M.K.; Venkatesan, Ramarathnam

doi:10.1007/3-540-47870-1_2

Cited by 97 publications

(43 citation statements)

References 3 publications

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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%

“…Finally, the binary perceptual hash string is compressed and encrypted into a short and a final perceptual hash in the Compression and Encryption stage (Figure 1). (Mihçak & R.Venkatesan, 2001) is another quantization type which is is the most famous quantization scheme in the field of image hashing. The difference between the two quantization schemes is that the partition of uniform quantization is based on the interval length of the hash values, whereas the partition of adaptive quantization is based on the probability density function (pdf) of the hash values.…”

Section: Perceptual Image Hashing Frameworkmentioning

confidence: 99%

“…Equation (4) means that, given an image I, it should be nearly impossible for an adversary to construct a perceptually different image I diff such that H(I)=H(I diff ). This property can be hard to achieve because the features used by published perceptual hash functions are publicly known (Kerckhoffs, 1883;Mihçak & R.Venkatesan, 2001). Also, it makes property in equation (3) be neglected in favor of property in equation (4).…”

Section: Metrics and Important Requirements Of A Perceptual Image Hasmentioning

confidence: 99%

See 2 more Smart Citations

Perceptual Image Hashing

Hadmi¹,

Puech²,

Said³

et al. 2012

Watermarking - Volume 2

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Section: Perceptual Image Hashing Methods Classificationmentioning

confidence: 99%

Section: Perceptual Image Hashing Frameworkmentioning

confidence: 99%

Section: Metrics and Important Requirements Of A Perceptual Image Hasmentioning

confidence: 99%

See 1 more Smart Citation

Perceptual Image Hashing

Hadmi¹,

Puech²,

Said³

et al. 2012

Watermarking - Volume 2

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“…The common preprocessing operations include image downsampling [7], low-pass filtering for reducing high frequency signals [8], resizing for image rescaling, order statistic filtering for denoising [9], and Gaussian blurring [10]. As such, all above operations serve for the next essential attributes extraction stage.…”

Section: General Framework Of Image Hashingmentioning

confidence: 99%

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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“…In order to efficiently identify digital images, perceptual hash techniques have been used [1][2][3]. A hash value, typically a short binary string, is generated to act as a unique identifier of the corresponding image.…”

Section: Introductionmentioning

confidence: 99%

Radon Transform-Based Secure Image Hashing

Nguyen

Weng

Preneel

2011

Communications and Multimedia Security

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Abstract. This paper presents a robust and secure image hash algorithm. The algorithm extracts robust image features in the Radon transform domain. A randomization mechanism is designed to achieve good discrimination and security. The hash value is dependent on a secret key. We evaluate the performance of the proposed algorithm and compare the results with those of one existing Radon transform-based algorithm. We show that the proposed algorithm has good robustness against contentpreserving distortion. It withstands JPEG compression, filtering, noise addition as well as moderate geometrical distortions. Additionally, we achieve improved performance in terms of discrimination, sensitivity to malicious tampering and receiver operating characteristics. We also analyze the security of the proposed algorithm using differential entropy and confusion/diffusion capabilities. Simulation shows that the proposed algorithm well satisfies these metrics.

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New Iterative Geometric Methods for Robust Perceptual Image Hashing

Cited by 97 publications

References 3 publications

Perceptual Image Hashing

Perceptual Image Hashing

A Survey on Image Hashing for Image Authentication

Radon Transform-Based Secure Image Hashing

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