W3-privacy: understanding what, when, and where inference channels in multi-camera surveillance video

Saini, Mukesh; Atrey, Pradeep K.; Mehrotra, Sharad; Kankanhalli, Mohan

doi:10.1007/s11042-012-1207-9

Cited by 59 publications

(37 citation statements)

References 31 publications

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“…To prevent any breach of privacy, we currently use images of low resolution so that people's faces are not easily recognizable. For a more comprehensive approach towards privacy, the identity leakage model [29] could be used to calculate the privacy loss due to the face identity as well as the various side-channels. Furthermore, adaptive robust privacy protection methods [30] could be incorporated into our proposed framework.…”

Section: Discussionmentioning

confidence: 99%

Tweeting Cameras for Event Detection

Wang

Kankanhalli

2015

Proceedings of the 24th International Conference on World Wide Web

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We are living in a world of big sensor data. Due to the widespread prevalence of visual sensors (e.g. surveillance cameras) and social sensors (e.g. Twitter feeds), many events are implicitly captured in real-time by such heterogeneous "sensors". Combining these two complementary sensor streams can significantly improve the task of event detection and aid in comprehending evolving situations. However, the different characteristics of these social and sensor data make such information fusion for event detection a challenging problem. To tackle this problem, we propose an innovative multi-layer tweeting cameras framework integrating both physical sensors and social sensors to detect various concepts of real-world events. In this framework, visual concept detectors are applied on camera video frames and these concepts can be construed as "camera tweets" posted regularly. These tweets are represented by a unified probabilistic spatio-temporal (PST) data structure which is then aggregated to a concept-based image (Cmage) as the common representation for visualization. To facilitate event analysis, we define a set of operators and analytic functions that can be applied on the PST data by the user to discover occurrences of events and to analyse evolving situations. We further leverage on geo-located social media data by mining current topics discussed on Twitter to obtain the high-level semantic meaning of detected events in images. We quantitatively evaluate our framework with a large-scale dataset containing images from 150 New York real-time traffic CCTV cameras, university foodcourt camera feeds and Twitter data, which demonstrates the feasibility and effectiveness of our proposed framework. Results of combining camera tweets and social tweets are shown to be promising for detecting real-world events.

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Section: Discussionmentioning

confidence: 99%

Tweeting Cameras for Event Detection

Wang

Kankanhalli

2015

Proceedings of the 24th International Conference on World Wide Web

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“…For measuring the level of privacy, the failure rates of automatic face recognition methods (PCA [53], LDA [8], LBP [1]) were considered, while the accuracy rate of a face detector (Viola-Jones [54]) were used to measure intelligibility. In these experiments only faces were considered, which is insufficient for proper privacy protection taking into account the above mentioned privacy by context approach or the secondary (implicit) privacy channels described by Saini et al [46]. Our paper focuses on establishing an objective evaluation framework by exploiting various evaluator functions to measure privacy and utility in various aspects.…”

Section: Related Workmentioning

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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“…[30][31][32] Next, we provide a review of the existing subjective evaluation (Sec. 2.1) and objective evaluation methods (Sec.…”

Section: Related Workmentioning

confidence: 99%

“…30,34 Saini et al 31 computed privacy loss using a model that incorporated the face detection and face recognition measures in addition to the scene contextual knowledge and some implicit identity inference information. In another work, 32 privacy loss was modeled using only the scene contextual knowledge and implicit identity inference information. While interesting contributions, the above works are target dependent as they assume presence of a specific target type ("face") in the image data and rely also on the performance of detection and recognition algorithms used.…”

Section: Objective Evaluationmentioning

confidence: 99%

“…Unlike existing methods, 22,[25][26][27][28][29] the proposed method in this paper does not rely on subjective judgments. Additionally, unlike the methods, [30][31][32]34 the proposed method is target independent as it does not require application of privacy protection methods on image data with a particular target type. Moreover, the proposed method does not require target detection and recognition accuracies in the evaluation procedure as in existing methods [30][31][32]34 and is therefore annotation free.…”

Section: Objective Evaluationmentioning

confidence: 99%

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Effective evaluation of privacy protection techniques in visible and thermal imagery

Nawaz

Berg

Ferryman

et al. 2017

J. Electron. Imaging

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Abstract. Privacy protection may be defined as replacing the original content in an image region with a (less intrusive) content having modified target appearance information to make it less recognizable by applying a privacy protection technique. Indeed, the development of privacy protection techniques also needs to be complemented with an established objective evaluation method to facilitate their assessment and comparison. Generally, existing evaluation methods rely on the use of subjective judgments or assume a specific target type in image data and use target detection and recognition accuracies to assess privacy protection. An annotation-free evaluation method that is neither subjective nor assumes a specific target type is proposed. It assesses two key aspects of privacy protection: "protection" and "utility." Protection is quantified as an appearance similarity, and utility is measured as a structural similarity between original and privacy-protected image regions. We performed an extensive experimentation using six challenging datasets (having 12 video sequences), including a new dataset (having six sequences) that contains visible and thermal imagery. The new dataset is made available online for the community. We demonstrate effectiveness of the proposed method by evaluating six image-based privacy protection techniques and also show comparisons of the proposed method over existing methods.

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W3-privacy: understanding what, when, and where inference channels in multi-camera surveillance video

Cited by 59 publications

References 31 publications

Tweeting Cameras for Event Detection

Tweeting Cameras for Event Detection

Privacy protection vs. utility in visual data

Effective evaluation of privacy protection techniques in visible and thermal imagery

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