A New Curvelet Based Blind Semi-fragile Watermarking Scheme for Authentication and Tamper Detection of Digital Images

Nirmala, S. Jaya; Chetan, K. R.

doi:10.1007/978-81-322-2752-6_2

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Acorpusofaround200imagesofdifferenttypeshasbeencreatedfortestingtheproposedadaptive semi-fragilewatermarkingsystem.MostoftheseimagesarescanneddocumentimageslikeCheque, Marks-cards,IdentificationandsomeimagesaretakenfromthestandardimagedatabaseUSC-SIPI (USC,n.d.). The tamper detection results of the existing method (Nirmala & Chetan, 2016) .…”

Section: Resultsmentioning

confidence: 99%

An Adaptive Curvelet Based Semi-Fragile Watermarking Scheme for Effective and Intelligent Tampering Classification and Recovery of Digital Images

Chetan

Nirmala

2018

International Journal of Rough Sets and Data Analysis

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A novel adaptive semi-fragile watermarking scheme for tamper detection and recovery of digital images is proposed in this paper. This scheme involves embedding of content and chroma watermarks generated from the first level Discrete Curvelet Transform (DCLT) coarse coefficients. Embedding is performed by quantizing the first level coarse DCLT coefficients of the input image and amount of quantization is intelligently decided based on the energy contribution of the coefficients. During watermark extraction, a tampered matrix is generated by comparing the feature similarity index value between each block of extracted and generated watermarks. The tampered objects are subsequently identified and an intelligent report is formed based on their severity classes. The recovery of the tampered objects is performed using the generated DCLT coefficients from luminance and chrominance components of the watermarked image. Results reveal that the proposed method outperforms existing method in terms of tamper detection and recovery of digital images.

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Section: Resultsmentioning

confidence: 99%

An Adaptive Curvelet Based Semi-Fragile Watermarking Scheme for Effective and Intelligent Tampering Classification and Recovery of Digital Images

Chetan

Nirmala

2018

International Journal of Rough Sets and Data Analysis

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“…Numerous methods, both heuristic and theoretical, have been developed to support the benefits of such sparse signal representations: in theoretical neuroscience it has been argued that sparse signal representations in an overcomplete dictionary are necessary for use in biological vision systems [26]; in approximation theory, it has been demonstrated that approximation from overcomplete systems outperforms any known basis [27]; in signal processing, learned overcomplete dictionaries from a set of realizations of the data (training signals) are highly adapted to the given class of signals and therefore usually exhibit good representation performance [28]; and in image processing, the learned dictionaries have shown promising results in several recently published works on compression of facial images [29], fingerprint images [30], geometry images of 3D face models [31], synthetic aperture radar (SAR) images [32], and hyperspectral images [33].…”

Section: Introductionmentioning

confidence: 99%

Greedy Signal Space Recovery Algorithm with Overcomplete Dictionaries in Compressive Sensing

Zhu,

Zhao,

Shi

et al. 2019

Preprint

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Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. To this end, we apply a signal space greedy method, which relies on the ability to optimally project a signal onto a small number of dictionary atoms, to address signal recovery in this setting. We describe a generalized variant of the iterative recovery algorithm called Signal space Subspace Pursuit (SSSP) for this more challenging setting. Here, using the Dictionary-Restricted Isometry Property (D-RIP) rather than classical RIP, we derive a low bound on the number of measurements required and then provide the proof of convergence for the algorithm. The algorithm in noisy and noise-free measurements has low computational complexity and provides high recovery accuracy. Simulation results show that the algorithm outperforms best compared with the existing recovery algorithms.

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“…Akin to this, in [ 13 ], the authors presented a perceptual hash algorithm for multispectral image authentication. In [ 14 ], the authors presented a semifragile, blind image authentication using discrete curvelet transform. The scheme was promising in terms of robustness and high normalized correlation.…”

Section: Introductionmentioning

confidence: 99%

Robust and Fragile Medical Image Watermarking: A Joint Venture of Coding and Chaos Theories

Rahman

Sultan

Musleh

et al. 2018

Journal of Healthcare Engineering

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A secure spatial domain, hybrid watermarking technique for obtaining watermark (authentication information) robustness and fragility of the host medical image (content integrity) using product codes, chaos theory, and residue number system (RNS) is proposed. The proposed scheme is highly fragile and unrecoverable in terms of the host image, but it is significantly robust and recoverable in terms of the watermark. Altering the medical image may result in misdiagnosis, hence the watermark that may contain patient information and organization logo must be protected against certain attacks. The host medical image is separated into two parts, namely, the region of interest (ROI) and region of noninterest (RONI) using a rectangular region. The RONI part is used to embed the watermark information. Moreover, two watermarks are used: one to achieve authenticity of image and the other to achieve the robustness against both incidental and malicious attacks. Effectiveness in terms of security, robustness, and fragility of the proposed scheme is demonstrated by the simulations and comparison with the other state-of-the-art techniques.

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A New Curvelet Based Blind Semi-fragile Watermarking Scheme for Authentication and Tamper Detection of Digital Images

Cited by 4 publications

References 12 publications

An Adaptive Curvelet Based Semi-Fragile Watermarking Scheme for Effective and Intelligent Tampering Classification and Recovery of Digital Images

An Adaptive Curvelet Based Semi-Fragile Watermarking Scheme for Effective and Intelligent Tampering Classification and Recovery of Digital Images

Greedy Signal Space Recovery Algorithm with Overcomplete Dictionaries in Compressive Sensing

Robust and Fragile Medical Image Watermarking: A Joint Venture of Coding and Chaos Theories

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