Privacy protection by concealing persons in circumstantial video image

Tansuriyavong, Suriyon; Hanaki, Shin-ichi

doi:10.1145/971478.971519

Cited by 48 publications

(30 citation statements)

References 5 publications

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“…More specifically, the filter G used is a 3 × 3 matrix of the form: Edge detection when applied to matrices U CA and V CA results in matrices U f inal and V f inal . Similar to the SVD-CA step, only a percentage of the resulting matrix is used in computing the output image according to (2), (3). The output of this individual step is show in Figure 5.…”

Section: ) Svd Modified Sobel Filtering (Svd-msf)mentioning

confidence: 99%

“…After solving 17 radius R can be calculated as: 2 2 , x i is a support vector or a i > 0} (18) With the above approach it is possible to calculate a good estimate of radius R that will provide with the required distortion to de-identify the input facial images.…”

Section: Projections Used For De-identifficationmentioning

confidence: 99%

“…These methods aim to destroy the majority, if not all, of the data concerning the depicted individual. A method developed by the authors in [1] and [2], de-identify persons in videos by de-identifying not only the face area but the person as a whole. Ad-hoc solutions facial images de-identification [8] include the use of simple methods.…”

Section: Introductionmentioning

confidence: 99%

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De-identifying facial images using singular value decomposition and projections

Chriskos

Zoidi

Tefas

et al. 2016

Multimed Tools Appl

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Abstract-In this article, a number of methods are analyzed that manipulate images in a manner that hinders face recognition by automatic recognition algorithms. The purpose of these methods, is to partly degrade image quality, so that humans can identify the person or persons in a scene, while common classification algorithms fail to do so. The approach used to achieve this involves the use of singular value decomposition (SVD) and projections on hyperspheres. From experiments it can be concluded that, these methods reduce the percentage of correct classification rate by over 90% . In addition, the final image is not degraded beyond recognition by humans.

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Section: ) Svd Modified Sobel Filtering (Svd-msf)mentioning

confidence: 99%

Section: Projections Used For De-identifficationmentioning

confidence: 99%

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De-identifying facial images using singular value decomposition and projections

Chriskos

Zoidi

Tefas

et al. 2016

Multimed Tools Appl

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“…This paper focuses on an objective evaluation method due to its advantages over a subjective evaluation such as the support for automatic operation (no human assessment required), the reduced costs of implementation, and the increased reproducibility. Many techniques have been proposed for visual privacy protection [3, 5, 15, 18-20, 22, 29, 30, 34-36, 40, 41, 49, 59], but only a few papers have been published on how to evaluate, assess or compare these techniques [6,11,17,27,31,45,51,52]. The main motivation behind this work was therefore to comprehensively explore the objective evaluation of the privacy-utility design space for visual privacy filters.…”

Section: Introductionmentioning

confidence: 99%

Privacy protection vs. utility in visual data

Erdélyi

Winkler

Rinner

2017

Multimed Tools Appl

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Ubiquitous and networked sensors impose a huge challenge for privacy protection which has become an emerging problem of modern society. Protecting the privacy of visual data is particularly important due to the omnipresence of cameras, and various protection mechanisms for captured images and videos have been proposed. This paper introduces an objective evaluation framework in order to assess such protection methods. Visual privacy protection is typically realised by obfuscating sensitive image regions which often results in some loss of utility. Our evaluation framework assesses the achieved privacy protection and utility by comparing the performance of standard computer vision tasks, such as object recognition, detection and tracking on protected and unprotected visual data. The proposed framework extends the traditional frame-by-frame evaluation approach by introducing two new approaches based on aggregated and fused frames. We demonstrate our framework on eight differently protected video-sets and measure the trade-off between the improved privacy protection due to obfuscating captured image data and the degraded utility of the visual data. Results provided by our objective evaluation method are compared with an available state-of-the-art subjective study of these eight protection techniques.

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“…Many systems for video privacy protection have been proposed for applications with specific tasks, such as video surveillance [1]- [4], Google Street View [5], [6], and lifelogging [7]. These systems basically detect all or a predetermined set of persons and obscure them using such appearance obscuration methods as blurring and blocking out.…”

Section: Introductionmentioning

confidence: 99%

Privacy Protection for Social Video via Background Estimation and CRF-Based Videographer's Intention Modeling

Nakashima

Babaguchi

Fan

2016

IEICE Trans. Inf. & Syst.

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SUMMARYThe recent popularization of social network services (SNSs), such as YouTube, Dailymotion, and Facebook, enables people to easily publish their personal videos taken with mobile cameras. However, at the same time, such popularity has raised a new problem: video privacy. In such social videos, the privacy of people, i.e., their appearances, must be protected, but naively obscuring all people might spoil the video content. To address this problem, we focus on videographers' capture intentions. In a social video, some persons are usually essential for the video content. They are intentionally captured by the videographers, called intentionally captured persons (ICPs), and the others are accidentally framed-in (nonICPs). Videos containing the appearances of the non-ICPs might violate their privacy. In this paper, we developed a system called BEPS, which adopts a novel conditional random field (CRF)-based method for ICP detection, as well as a novel approach to obscure non-ICPs and preserve ICPs using background estimation. BEPS reduces the burden of manually obscuring the appearances of the non-ICPs before uploading the video to SNSs. Compared with conventional systems, the following are the main advantages of BEPS: (i) it maintains the video content, and (ii) it is immune to the failure of person detection; false positives in person detection do not violate privacy. Our experimental results successfully validated these two advantages. key words: intentionally captured person, conditional random field, background estimation, privacy protection, social video

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Privacy protection by concealing persons in circumstantial video image

Cited by 48 publications

References 5 publications

De-identifying facial images using singular value decomposition and projections

De-identifying facial images using singular value decomposition and projections

Privacy protection vs. utility in visual data

Privacy Protection for Social Video via Background Estimation and CRF-Based Videographer's Intention Modeling

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