A Novel Image Authentication with Tamper Localization and Self-Recovery in Encrypted Domain Based on Compressive Sensing

Zhang, Rui; Xiao, Di; Chang, Yanting

doi:10.1155/2018/1591206

Cited by 13 publications

(8 citation statements)

References 33 publications

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“…Grouping is different in Table 2 using the frequency domain. Method [4,41,49,54] take advantage of robust watermarking for generating recovery bits inserted into the LL sub band on the DWT. The main drawback of this method is that computing is quite high and the watermark insertion capacity is low.…”

Section: Comparison and Discussionmentioning

confidence: 99%

A recent survey of self-embedding fragile watermarking scheme for image authentication with recovery capability

Rakhmawati

Wirawan

Suwadi

2019

J Image Video Proc.

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The rapid developments of storage technology and information exchange encourage the development of research in the field of information security. In the process of sending information, tamper and issues about data ownership may occur. The fragile watermarking is one technique that can overcome the problem, in which this technique exploits sensitivity to tamper with the inserted watermark components. Therefore, it is not tolerable to change even only one bit. Self-embedding can be defined as some important features obtained from a selected image as a watermark by modifying the pixel value of the original image. Once a picture which has been inserted with a watermark is modified by other users, it can be used for tamper detection and recovery images. Due to the importance of this fragile watermarking scheme, this paper discusses the principles and characteristics of a fragile watermarking algorithm. The main contribution of this paper survey is that it summarises the current mechanisms of selection, generation, method of watermark insertion, detection and tamper localisation and recovery procedures. Comparison of several watermarking techniques was analysed and presented in tabular form, as well as experimental evaluation of four watermarking schemes in many graphs to show the performance of the self-embedding fragile watermarking scheme.

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Section: Comparison and Discussionmentioning

confidence: 99%

A recent survey of self-embedding fragile watermarking scheme for image authentication with recovery capability

Rakhmawati

Wirawan

Suwadi

2019

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…With the rapid development of data storage and digital process, more and more digital information is transformed and transmitted over the Internet day by day, which brings people a series of security problems as well as convenience [42]. The researches on digital image security, that is, image encryption, image data hiding, and image authentication, become more important than ever [38,39].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Hash for Wireless Multimedia Image Content Security

Zheng

Zhu

Fang

et al. 2018

Security and Communication Networks

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With the explosive growth of the wireless multimedia data on the wireless Internet, a large number of illegal images have been widely disseminated in wireless networks, which seriously endangers the content security of wireless networks. However, how to identify and classify illegal images quickly, accurately, and in real time is a key challenge for wireless multimedia networks. To avoid illegal images circulating on the Internet, each image needs to be detected, extracted features, and compared with the image in the feature library to verify the legitimacy of the image. An improved image deep learning hash (IDLH) method to learn compact binary codes for image search is proposed in this paper. Specifically, there are three major processes of IDLH: the feature extraction, deep secondary search, and image classification. IDLH performs image retrieval by the deep neural networks (DNN) as well as image classification with the binary hash codes. Different from other deep learning-hash methods that often entail heavy computations by using a conventional classifier, exemplified by K nearest neighbor (K-NN) and support vector machines (SVM), our method learns classifiers using binary hash codes, which can be learned synchronously in training. Finally, comprehensive experiments are conducted to evaluate IDLH method by using CIFAR-10 and Caltech 256 image library datasets, and the results show that the retrieval performance of IDLH method can effectively identify illegal images.

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“…In [15], researchers proposed an oblivious watermarking technique to detect the tampering of digital images. In [16], a novel tamper detection, localization, and recovery scheme were proposed based on DWT and CS. In [17], a passive blind video similar tamper detection algorithm based on multi-scale normalized mutual information is proposed, which realizes video frame replication, frame insertion and frame deletion tamper detection.…”

Section: Introductionmentioning

confidence: 99%

Privacy Protection: An Anti-Fraud Scheme Based on Improved Bayesian Game Model in Multimedia Communication System

et al. 2020

Self Cite

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In the multimedia communication system, users are often exposed to fraud information from malicious applications (such as providing fake bids and false content), which can easily cause privacy leakage. And multimedia information pluralism make it more difficult to protect users' privacy. To solve these problems, this article proposed an anti-fraud scheme based on improved Bayesian game model. First, we designed a Bargain-bayesian game model for modeling the interaction between applications and regular users. We used the Two-round bargain method to establish payoff matrix for two players and adjust it through regular user's detection rate dynamically. Then we obtained the application's best bid in the first round of bargain by backward induction to prevent malicious applications from sending fake bids. Second, we customized a group of test users and developed another interaction model between applications and users (i.e., regular and test) as the Two-side Bayesian game model based on sliding adaptive logistic regression method, then we put three influencing factors into the payoff matrix: test user's ratio, malicious application's ratio, and regular user's ratio. Through Bayesian Nash Equilibria analysis, we obtained values of three influencing factors when malicious applications provide true content, and thus solved the problem of malicious applications providing false content. Finally, experiment results proved that the new scheme had effectively raised expected payoffs for both players and their transaction achievement rate, and lowered the probability of users being deceived by malicious applications, which successfully solved the issue of users' privacy disclosure.

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A Novel Image Authentication with Tamper Localization and Self-Recovery in Encrypted Domain Based on Compressive Sensing

Cited by 13 publications

References 33 publications

A recent survey of self-embedding fragile watermarking scheme for image authentication with recovery capability

A recent survey of self-embedding fragile watermarking scheme for image authentication with recovery capability

Deep Learning Hash for Wireless Multimedia Image Content Security

Privacy Protection: An Anti-Fraud Scheme Based on Improved Bayesian Game Model in Multimedia Communication System

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