Recent development of perceptual image hashing

Wang, Shuozhong; Zhang, Xinpeng

doi:10.1007/s11741-007-0401-2

Cited by 21 publications

(3 citation statements)

References 33 publications

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“…Radial variance hashing judges whether two images are similar by taking the global hash of the images and comparing the Hamming distance of the hashes of two images. The more similar the two images are, the smaller is the Hamming distance of the hash values (Shuo-zhong and Xin-peng, 2007 ). The principle of the hash value calculation algorithm is to perform lossy compression on the original data.…”

Section: Research Methodology and Data Sourcesmentioning

confidence: 99%

Predicting the distribution of COVID-19 through CGAN—Taking Macau as an example

et al. 2023

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Machine learning (ML) is an innovative method that is widely used in data prediction. Predicting the COVID-19 distribution using ML is essential for urban security risk assessment and governance. This study uses conditional generative adversarial network (CGAN) to construct a method to predict the COVID-19 hotspot distribution through urban texture and business formats and establishes a relationship between urban elements and COVID-19 so that machines can automatically predict the epidemic hotspots in cities. Taking Macau as an example, this method is used to determine the correlation between the urban texture and business hotspots of Macau and the new epidemic hotspot clusters. Different types of samples afforded different epidemic prediction accuracies. The results show the following: (1) CGAN can accurately predict the distribution area of COVID-19, and the accuracy can exceed 70%. (2) The results of predicting the COVID-19 distribution through urban texture and POI data of hospitals and stations are the best, with an accuracy of more than 60% in experiments in different regions of Macau. (3) The proposed method can also predict other areas in the city that may be at risk of COVID-19 and help urban epidemic prevention and control.

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Section: Research Methodology and Data Sourcesmentioning

confidence: 99%

Predicting the distribution of COVID-19 through CGAN—Taking Macau as an example

et al. 2023

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“…Therefore, it is necessary to create new tools and techniques to discover the authenticity and integrity of digital media [1]. In recent decade, several methods have been extensively studied for intellectual property protection of digital images and image forgeries detection including image watermarking-based schemes [2], digital image forensic-based schemes [3] and perceptual image hashing-based schemes [4,5]. For security/authentication of multimedia data, perceptual image hashing is introduced based on traditional cryptosystems.…”

Section: Introductionmentioning

confidence: 99%

Image authentication using LBP-based perceptual image hashing

Davarzani

Mozaffari

Yaghmaie

2015

JAIDM

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Feature extraction is a main step in all perceptual image hashing schemes in which robust features will lead to better results in perceptual robustness. Simplicity, discriminative power, computational efficiency and robustness to illumination changes are counted as distinguished properties of Local Binary Pattern features. In this paper, we investigate the use of local binary patterns for perceptual image hashing. In feature extraction, we propose to use both sign and magnitude information of local differences. So, the algorithm utilizes a combination of gradient-based and LBP-based descriptors for feature extraction. To provide security needs, two secret keys are incorporated in feature extraction and hash generation steps. Performance of the proposed hashing method is evaluated with an important application in perceptual image hashing scheme: image authentication. Experiments are conducted to show that the present method has acceptable robustness against perceptual content-preserving manipulations. Moreover, the proposed method has this capability to localize the tampering area, which is not possible in all hashing schemes.

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“…Perceptual hash has the characteristics that it should remain the same if the images are perceptually looked the same, while different for different images and if unacceptable changes are caused to the images. These characteristics, also named as robustness and discrimination, are very desirable in the case of image authentication (Wang and Zhang 2007). The relationship of cryptographic hash, image hash and perceptual hash is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Image authentication based on perceptual hash using Gabor filters

et al. 2009

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Image authentication is an important topic in image forensics, which tells whether an image is tampered or not or even tells the tampered regions. To implement image authentication, image hash techniques have been reported recently. In this paper, we investigate existing image hash algorithms, and design an novel image hash based on human being's visual system. In this algorithm, we capture the perceptual characters of the image using Gabor filter which can sense the directions in the image just like human's primary visual cortex. For a given image, we compute the reference scale, direction and block to make sure the final hash can resist against rotation, scale, and translation attacks while maintain the sensitivity to local malicious manipulations. In addition, it has another promising ability to locate the tampered image blocks, and approximately determining the type of tampering methods (delete, add, modify) and the original direction of each block. This ability is very useful in forensics. The experimental results show that the strategy of the reference metrics works quite well and our method is much more effective than the other state of art image hash methods. Moreover, our method can still locate the contentaltering changes even undergo some content-preserving manipulations.

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Recent development of perceptual image hashing

Cited by 21 publications

References 33 publications

Predicting the distribution of COVID-19 through CGAN—Taking Macau as an example

Predicting the distribution of COVID-19 through CGAN—Taking Macau as an example

Image authentication using LBP-based perceptual image hashing

Image authentication based on perceptual hash using Gabor filters

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