Quaternion-Based Image Hashing for Adaptive Tampering Localization

Yan, Cai-Ping; Pun, Chi‐Man; Yuan, Xiaochen

doi:10.1109/tifs.2016.2594136

Cited by 69 publications

(31 citation statements)

References 24 publications

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“…RPIVD-based image hashing [11] incorporates ring partition and invariant vector distance to image hashing algorithm for enhancing rotation robustness and discriminative capability. Quaternion-based image hashing [5] constructs quaternion image, which combines advantages of both color and structural features, to implement the quaternion Fourier transform for image feature hashing generation. We report the the tamper detection results due to our emphasis.…”

Section: Comparison Resultsmentioning

confidence: 99%

“…We report the the tamper detection results due to our emphasis. Table 1 shows the probability of true authentication capability of the proposed method compared to the methods proposed in [1,5,8,11]. Note that the region size in our method is set as 64 × 64.…”

Section: Comparison Resultsmentioning

confidence: 99%

“…In conventional image hashing methods, image hash generation is a robust feature compression process without any learning stage. It includes (1) invariant feature transform based methods, such as Wavelet transform [1], Radon transform [2], Fourier-Mellin transform [3], DCT transform [4], and QFT transform [5], which aim to extract robust features from transform domains; (2) local feature points based methods, such as SIFT [6] and end-stopped wavelet [7], which take advantages of the invariant local feature under some content preserving image processing attacks; (3) dimension reduction based methods, such as singular value decomposition (SVD) [8], nonnegative matrix factorization (NMF) [9], and Fast Johnson-Lindenstrauss transform (FJLT) [10], which embed the low level features of the high dimensional space into lower dimension; (4) statistics features based methods, such as the robust image hashing with ring partition and invariant vector distance [11]. Moreover, Wang et al [12] propose a perceptual image hashing method by combining image block based features and key-point-based features.…”

Section: Introductionmentioning

confidence: 99%

“…Two real-valued intermediate hashing methods are adopted for learning. Likewise, Yan et al [5] proposed a quaternion-based image hashing for tampering localization. Four types of feature maps are selected for quaternion image formation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Saliency

Chen

2018

Advances in Multimedia

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Perceptual hashing technique for tamper detection has been intensively investigated owing to the speed and memory efficiency. Recent researches have shown that leveraging supervised information could lead to learn a high-quality hashing code. However, most existing methods generate hashing code by treating each region equally while ignoring the different perceptual saliency relating to the semantic information. We argue that the integrity for salient objects is more critical and important to be verified, since the semantic content is highly connected to them. In this paper, we propose a Multi-View Semi-supervised Hashing algorithm with Perceptual Saliency (MV-SHPS), which explores supervised information and multiple features into hashing learning simultaneously. Our method calculates the image hashing distance by taking into account the perceptual saliency rather than directly considering the distance value between total images. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.

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Section: Comparison Resultsmentioning

confidence: 99%

Section: Comparison Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Saliency

Chen

2018

Advances in Multimedia

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“…First, the quaternion Fourier-Mellin transform (QFMT) is used to calculate the geometric hash to eliminate the influence of geometric distortions. Then, a new quaternion image construction approach, which combines both color and structural features benefit, is proposed to implement quaternion Fourier transform (QFT) to calculate the image feature hash to locate the tampered regions [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Image Tampering Using Image Hashing

Jha¹,

Tiwari²

2017

IJARCSSE

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Abstract-Due to powerful computers and advanced image-editing software tools the images manipulation has become a simple task. Authenticate images authenticity and tampered regions from image detecting without any information about image content. An effort is made to survey the recent advancements being made in the field of digital image forgery detection and thus passive methods for forgery detection are being presented. In this paper Gaussian, salt and pepper, sprinkle and find histogram with hash generation and then find better result as compare base work.

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Image splicing forgery detection by combining synthetic adversarial networks and hybrid dense U‐net based on multiple spaces

Yang

Wang

et al. 2022

Int J of Intelligent Sys

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With the popularity of image editing tools, the originality and information security of images are facing serious threats. The most common threat is splicing forgery that copies a part of the area from one donor image to the acceptor one. Some research works were proposed to protect the image originality, whereas they are still difficult to apply in practice.There are two main reasons: (a) very limited data for learning models; (b) huge attribute differences between the donor and acceptor images. We propose two novel tasks to conquer the above challenges: Synthetic Adversarial Networks (SANs) and Hybrid Dense U-Net (HDU-Net). SAN finds the most secluded position for inserting tampered areas in an image by learning the association between scenes and objects, and can enlarge the original small data set by more than 40 times. We call the data set SF-Data generated by SAN. We combine the dense U-Net that detects the differences of the essential attributes of image with four spaces containing more available feature information to propose HDU-Net. Then, the synthetic data set SF-Data are used to train HDU-Net. We perform various attack experiments on several public data sets to demonstrate the effectiveness and robustness of our method.

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Quaternion-Based Image Hashing for Adaptive Tampering Localization

Cited by 69 publications

References 24 publications

Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Saliency

Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Saliency

A Novel Approach for Image Tampering Using Image Hashing

Image splicing forgery detection by combining synthetic adversarial networks and hybrid dense U‐net based on multiple spaces

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