Robust hashing for image authentication using quaternion discrete Fourier transform and log-polar transform

Ouyang, Jianquan; Coatrieux, Gouenou; Hu, Shu

doi:10.1016/j.dsp.2015.03.006

Cited by 66 publications

(39 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of a watermarking technique is estimated in terms of robustness, imperceptibility, capacity, and detachment detection. Another paper [90] proposed the combination of the QDFT and a log-polar transform, where the QDFT is the quaternion discrete Fourier transform. The QDFT deals with the three channels (red, green, and blue) of color images.…”

Section: Of 38mentioning

confidence: 99%

Digital Image Watermarking Techniques: A Review

2020

View full text Add to dashboard Cite

Digital image authentication is an extremely significant concern for the digital revolution, as it is easy to tamper with any image. In the last few decades, it has been an urgent concern for researchers to ensure the authenticity of digital images. Based on the desired applications, several suitable watermarking techniques have been developed to mitigate this concern. However, it is tough to achieve a watermarking system that is simultaneously robust and secure. This paper gives details of standard watermarking system frameworks and lists some standard requirements that are used in designing watermarking techniques for several distinct applications. The current trends of digital image watermarking techniques are also reviewed in order to find the state-of-the-art methods and their limitations. Some conventional attacks are discussed, and future research directions are given.

show abstract

Section: Of 38mentioning

confidence: 99%

Digital Image Watermarking Techniques: A Review

2020

View full text Add to dashboard Cite

show abstract

“…It can also detect cut and paste attacks. Ouyang et al [11] used Quaternion Discrete Fourier Transforms (QDFT) and Log polar Transform to generate hash for color images. This scheme shows good sensitivity and is robust against content preserving manipulations, especially large angle rotation.…”

Section: Literature Reviewmentioning

confidence: 99%

A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids

Bashir

Ahmed

Ahmad

et al. 2019

Entropy

View full text Add to dashboard Cite

Image hash is an alternative to cryptographic hash functions for checking integrity of digital images. Compared to cryptographic hash functions, an image hash or a Perceptual Hash Function (PHF) is resilient to content preserving distortions and sensitive to malicious tampering. In this paper, a robust and secure image hashing technique using a Gaussian pyramid is proposed. A Gaussian pyramid decomposes an image into different resolution levels which can be utilized to obtain robust and compact hash features. These stable features have been utilized in the proposed work to construct a secure and robust image hash. The proposed scheme uses Laplacian of Gaussian (LOG) and disk filters to filter the low-resolution Gaussian decomposed image. The filtered images are then subtracted and their difference is used as a hash. To make the hash secure, a key is introduced before feature extraction, thus making the entire feature space random. The proposed hashing scheme has been evaluated through a number of experiments involving cases of non-malicious distortions and malicious tampering. Experimental results reveal that the proposed hashing scheme is robust against non-malicious distortions and is sensitive to detect minute malicious tampering. Moreover, False Positive Probability (FPP) and False Negative Probability (FNP) results demonstrate the effectiveness of the proposed scheme when compared to state-of-the-art image hashing algorithms proposed in the literature.

show abstract

“…In digital signature based schemes, a hash of the image is captured and asymmetrically encrypted for non-repudiation purpose [7] and transmitted along with the image. At the reception, if the attached signature does not match the recomputed one, the image integrity and origin are not correct.…”

Section: Introductionmentioning

confidence: 99%

Computed Tomography Image Origin Identification Based on Original Sensor Pattern Noise and 3-D Image Reconstruction Algorithm Footprints

Duan

Bouslimi

Yang

et al. 2017

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

In this paper, we focus on the "blind" identification of the computed tomography (CT) scanner that has produced a CT image. To do so, we propose a set of noise features derived from the image chain acquisition and which can be used as CT-scanner footprint. Basically, we propose two approaches. The first one aims at identifying a CT scanner based on an original sensor pattern noise (OSPN) that is intrinsic to the X-ray detectors. The second one identifies an acquisition system based on the way this noise is modified by its three-dimensional (3-D) image reconstruction algorithm. As these reconstruction algorithms are manufacturer dependent and kept secret, our features are used as input to train a support vector machine (SVM) based classifier to discriminate acquisition systems. Experiments conducted on images issued from 15 different CT-scanner models of 4 distinct manufacturers demonstrate that our system identifies the origin of one CT image with a detection rate of at least 94% and that it achieves better performance than sensor pattern noise (SPN) based strategy proposed for general public camera devices.

show abstract

Robust hashing for image authentication using quaternion discrete Fourier transform and log-polar transform

Cited by 66 publications

References 33 publications

Digital Image Watermarking Techniques: A Review

Digital Image Watermarking Techniques: A Review

A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids

Computed Tomography Image Origin Identification Based on Original Sensor Pattern Noise and 3-D Image Reconstruction Algorithm Footprints

Contact Info

Product

Resources

About