One-Time Key Based Phase Scrambling for Phase-Only Correlation between Visually Protected Images

Ito, Izumi; Kiya, Hitoshi

doi:10.1155/2009/841045

Cited by 27 publications

(19 citation statements)

References 4 publications

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“…Ito & Kiya (2009) presented an image matching algorithm using phase-only correlation (POC) in the frequency domain. This algorithm preserves the visual privacy of the images in a template database.…”

Section: Secure Processingmentioning

confidence: 99%

Visual privacy protection methods: A survey

Padilla-López

Chaaraoui

Flórez-Revuelta

2015

Expert Systems with Applications

170

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Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambientassisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual's privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users' acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people's privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.

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Section: Secure Processingmentioning

confidence: 99%

Visual privacy protection methods: A survey

Padilla-López

Chaaraoui

Flórez-Revuelta

2015

Expert Systems with Applications

170

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“…Various methods have been proposed for privacy-preserving computation. The methods are classified into two types: perceptual encryption-based [5][6][7][8][9][10][11][12][13][14][15][16] and homomorphic encryption (HE)based [17][18][19][20][21][22][23][24]. As described in Section 2, HE-based methods are the most secure options for privacy preserving computation, but they This work was partially supported by Grant-in-Aid for Scientific Research(B), No.17H03267, from the Japan Society for the Promotion Science.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, data augmentation has to be done before encryption. In contrast, perceptual encryption-based methods have been seeking a trade-off in security to enable other requirements, such as a low processing demand, bitstream compliance, and signal processing in the encrypted domain [5][6][7][8][9][10][11][12][13][14][15][16]. A few methods were applied to machine learning algorithms in previous works [5,6].…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Deep Neural Networks with Pixel-Based Image Encryption Considering Data Augmentation in the Encrypted Domain

Sirichotedumrong

Maekawa

Kinoshita

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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We present a novel privacy-preserving scheme for deep neural networks (DNNs) that enables us not to only apply images without visual information to DNNs for both training and testing but to also consider data augmentation in the encrypted domain for the first time. In this paper, a novel pixel-based image encryption method is first proposed for privacy-preserving DNNs. In addition, a novel adaptation network is considered that reduces the influence of image encryption. In an experiment, the proposed method is applied to a well-known network, ResNet-18, for image classification. The experimental results demonstrate that conventional privacy-preserving machine learning methods including the state-of-the-arts cannot be applied to data augmentation in the encrypted domain and that the proposed method outperforms them in terms of classification accuracy.

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“…For securing multimedia data, full encryption with a state-of-the-art cipher (like RSA, AES, etc) is the most secure option. However, many multimedia applications have been seeking a trade-off in security to enable other requirements, e.g., low processing demands, retaining bitstream compliance, and signal process ing in the encrypted domain, so a lot of perceptual encryptions have been studied as one of schemes for achieving the trade off [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An encryption-then-compression system for JPEG standard

Kurihara

Shiota

Kiya

2015

2015 Picture Coding Symposium (PCS)

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In many multimedia applications, image encryption has to be conducted prior to image compression. This paper proposes an Encryption-then-Compression system using a JPEG friendly perceptual encryption method, which enables to be conducted prior to JPEG compression. The proposed encryption method can provides approximately the same compression per formance as that of JPEG compression without any encryption, where both gray scale images and color ones are considered. It is also shown that the proposed system consists of four block-based encryption steps, and provide a reasonably high level of security.Most of conventional perceptual encryption methods have not been designed for international compression standards, but this paper focuses on applying the JPEG standard, as one of the most widely used image compression standards.

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One-Time Key Based Phase Scrambling for Phase-Only Correlation between Visually Protected Images

Cited by 27 publications

References 4 publications

Visual privacy protection methods: A survey

Visual privacy protection methods: A survey

Privacy-Preserving Deep Neural Networks with Pixel-Based Image Encryption Considering Data Augmentation in the Encrypted Domain

An encryption-then-compression system for JPEG standard

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