Skin detection and lightweight encryption for privacy protection in real-time surveillance applications

Shifa, Amna; Imtiaz, Muhammad Babar; Asghar, Mamoona Naveed; Fleury, Martin

doi:10.1016/j.imavis.2019.103859

Cited by 19 publications

(13 citation statements)

References 39 publications

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“…Skin detection has been a long-standing challenge in the field of computer vision, and it has been used in many studies that involve decision-making about humans [ 33 , 38 ]. Several survey papers have explored various methods for skin detection [ 19 , 32 ].…”

Section: Proposed Solutionmentioning

confidence: 99%

A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment

Nadian-Ghomsheh

Farahani

Kavian

2021

Multimed Tools Appl

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The healthcare industry requires the integration of digital technologies, such as Artificial Intelligence (AI) and the Internet of Things (IoT), to their full potential, particularly during this challenging time and the recent outbreak of the COVID-19 pandemic, which resulted in the disruptions in healthcare delivery, service operations, and shortage of healthcare personnel. However, every opportunity has barriers and bumps, and when it comes to IoT healthcare, data privacy is one of the main growing issues. Despite the recent advances in the development of IoT healthcare architectures, most of them are invasive for the data subjects. In this context, the broad applications of AI in the IoT domain have also been hindered by emerging strict legal and ethical requirements to protect individual privacy. Camera-based solutions that monitor human subjects in everyday settings, e.g., for Online Range of Motion (ROM) detection, are making this problem even worse. One actively practiced branch of such solutions is telerehabilitation, which provides remote solutions for the physically impaired to regain their strength and get back to their normal daily routines. The process usually involves transmitting video/images from the patient performing rehabilitation exercises and applying Machine Learning (ML) techniques to extract meaningful information to help therapists devise further treatment plans. Thereby, real-time measurement and assessment of rehabilitation exercises in a reliable, accurate, and Privacy-Preserving manner is imperative. To address the privacy issue of existing solutions, this paper proposes a holistic Privacy-Preserving (PP) hierarchical IoT solution that simultaneously addresses the utilization of AI-driven IoT and the demands for data protection. Furthermore, the efficiency of the proposed architecture is demonstrated by a novel machine learning-based system that allows immediate assessment and extraction of ROM as the critical information for analyzing the progress of patients.

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Section: Proposed Solutionmentioning

confidence: 99%

A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment

Nadian-Ghomsheh

Farahani

Kavian

2021

Multimed Tools Appl

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“…In the method, the mean of the pixels located in the detected target In general, a mosaic block has a rectangular shape in which all pixels have the same color. In this study, a mosaic is generated in the unit of blocks [54,56] rather than the unit of pixels. In this case, it is important to let the mosaic overlap a detected target object region properly in terms of position and size.…”

Section: Blocking Through Mosaic Processingmentioning

confidence: 99%

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Jang

Lee

2020

Sustainability

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High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges as a social issue. This paper proposes an effective blocking technique that makes it possible to robustly detect target objects with exposed personal information from various types of input images with the use of deep neural computing and to effectively block the detected objects' regions. The proposed technique first utilizes the neural computing-based learning algorithm to robustly detect the target object including personal information from an image. It next generates a grid-type mosaic and lets the mosaic overlap the target object region detected in the previous step so as to effectively block the object region that includes personal information. Experimental results reveal that the proposed algorithm robustly detects the target object region with exposed personal information from a variety of input images and effectively blocks the detected region through grid-type mosaic processing. The object blocking technique proposed in this paper is expected to be applied to various application fields such as image security, sustainable anticipatory computing, object tracking, and target blocking.

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“…Apart from awareness of data usage among users, [11], [12] demonstrated how fragile an individuals' privacy is under state-of-the-art technology-enabled video surveillance. [6] states that any kind of human skin (face, skin tone) under the surveillance footage can behave as a feature that can lead to revealing the provenance of both, the video and people in the footage. There are many possibilities to mitigate some of the video privacy issues by using techniques like distorting images, adding noise, etc.…”

Section: Related Workmentioning

confidence: 99%

“…Also, surveillance systems should be proofed to reduce the risk of privacy infringement [4], which can for instance be achieved by employing visual anonymization techniques. While detection of individuals is simplified with current deep learning systems [6], applying visual modifications to the input (e.g., added noise) may confuse the neural network into making wrong predictions [7]. Besides, many of the available tracking technologies for real-time behavioral analysis (e.g., occupancy detection and in-store analytics [8]) are resourceand maintenance-heavy because of accuracy and correctness concerns.…”

Section: Introductionmentioning

confidence: 99%

Explicitly Privacy-Aware Space Usage Analysis

Jha

Mayer

Schneider

2020

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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Surveillance in private and public spaces provides observers with information that can enhance protection and efficiency but usually infringes upon the privacy of the individuals and groups. These informational privacy risks are centered on users' perceived and design-induced threats. They cannot be removed completely but can be minimized using suitable anonymization techniques. To minimize the users' informational privacy threats, we designed a privacy-aware surveillance system that gives the users leverage over the anonymization filters, to physically adjust the opaqueness of the camera lens used in the prototype according to their privacy requirements. We implement our prototype in the context of office space surveillance, where the proposed solution considers privacy requirements in such environments to improve users' trust in the surveillance system and reduce their privacy concerns.

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Skin detection and lightweight encryption for privacy protection in real-time surveillance applications

Cited by 19 publications

References 39 publications

A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment

A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Explicitly Privacy-Aware Space Usage Analysis

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