Under the Concealing Surface: Detecting and Understanding Live Webcams in the Wild

SongJinKe,; Liqiang,; WangHaining,; SunLimin,

doi:10.1145/3379471

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…Secondly, the new view of image synthesis is used to compose a new virtual environment. The all-seeing function is used to perform a set of all environment mappings for a given scene [23]. The allseeing function is defined as…”

Section: Methodsmentioning

confidence: 99%

Interaction Design of Wellness Building Space by Deep Learning and VR Technology in the Context of Internet of Things

Yang¹,

Wang²

2022

Wireless Communications and Mobile Computing

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This study explores the application of deep learning theory and virtual reality (VR) technology to the interactive behavior design of building space according to the interaction behavior of wellness building space in the context of the Internet of Things (IoT). Firstly, VR theory is made into an image-based 3-dimensional (3D) modeling process. Secondly, the interactive behavior information data is analyzed according to the theory of deep learning and edge computing. Finally, the particle swarm optimization (PSO) is used to analyze the predicted temperature with the wellness building space model, as well as to make a study based on the changes in the user’s psychological indicators. The results show that the model predictions of deep learning-edge computing are most like the actual environmental settings. Both PSO and deep learning algorithms have varying degrees of influence on the final prediction results. The average temperature of the wellness building space established by deep learning and edge computing is 24.58 degrees, the average measured value of the actual environment is 24.49 degrees, and the predicted values of the two are similar. While users experienced the interactive design of the health building, their heart rate dropped from 73.17 to 68.79 and gradually became stable. There is no obvious change in the user’s heart rate, which reflects the comfort of the wellness building space designed based on deep learning and VR interaction.

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Section: Methodsmentioning

confidence: 99%

Interaction Design of Wellness Building Space by Deep Learning and VR Technology in the Context of Internet of Things

Yang¹,

Wang²

2022

Wireless Communications and Mobile Computing

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“…GeoCAM relies on the websites that are hosting a large number of live webcams [15]. We collected those websites using a heuristic rule: if a website distributes live streams under web applets (e.g., Joint Photographic Experts Group (JPEG) [16], Motion JPEG (MJPEG), VideoLAN media player(VLC) [17], and FFMPEG [18]), we keep it as a candidate site.…”

Section: A Websitesmentioning

confidence: 99%

GeoCAM: An IP-Based Geolocation Service Through Fine-Grained and Stable Webcam Landmarks

Wang

Tan

et al. 2021

IEEE/ACM Trans. Networking

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IP-based geolocation is essential for various location-aware Internet applications, such as online advertisement, content delivery, and online fraud prevention. Achieving accurate geolocation enormously relies on the number of high-quality (i.e., the fine-grained and stable over time) landmarks. However, the previous efforts of garnering landmarks have been impeded by the limited visible landmarks on the Internet and manual time cost. In this paper, we leverage the availability of numerous online webcams used to monitor physical surroundings as a rich source of promising high-quality landmarks for serving IP-based geolocation. In particular, we present a new framework called GeoCAM, which is designed to automatically generate qualified landmarks from online webcams, providing an IP-based geolocation service with high accuracy and wide coverage. GeoCAM periodically monitors websites hosting live webcams and uses the natural language processing technique to extract the IP addresses and latitude/longitude of webcams for generating landmarks at a large-scale. Given latency and topology constraints among webcam landmarks, GeoCAM uses the maximum likelihood estimation to approximately pinpoint the geolocation of a target host. We develop a prototype of GeoCAM and conduct real-world experiments for validating its efficacy. Our results show that GeoCam can detect 282,902 live webcams hosted in webpages with 94.2% precision and 90.4% recall, and then generate 16,863 stable and fine-grained landmarks, which are two orders of magnitude more than the landmarks used in prior works. To demonstrate the superiority of using large-scale webcams as landmarks, we implement four different geolocation algorithms and compare their performance between webcam landmarks and open-source landmarks. The evaluation resultsshow that all the algorithms can significantly improve geolocation accuracy by using webcam landmarks.

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“…Xu et al [47] investigated IP cameras without password protection in insecam [7] to examine the vulnerabilities of those cameras. Song et al [42] conducted a systematic study on live webcams from both aggregation sites and individual webcams, and analysed the spatial and content features of live webcams. Our work differs from the above in two aspects: (i) These studies mainly rely on active measurements with a limited number of cameras, whereas our work leverages large-scale service logs of over 200K cameras from a major HSC service provider.…”

Section: Related Workmentioning

confidence: 99%

Characterising Usage Patterns and Privacy Risks of a Home Security Camera Service

Tyson

et al. 2020

IEEE Trans. on Mobile Comput.

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Home security cameras (HSCs) are becoming increasingly important in protecting people's household property and caring for family members. As an emerging type of home IoT devices, HSCs are distinct from traditional IoT devices in that they are often installed in intimate places, detecting movements constantly. Such close integration with users' daily life may result in distinct user behavioral patterns and privacy concerns. To explore this, we perform a detailed measurement study based on a large-scale service log dataset from a major HSC service provider. Our analysis reveals unique usage patterns of HSCs, including significant wasted uploads, asymmetrical upload and download traffic, skewed user engagement, and limited watching locations. We further identify three types of privacy risks in current HSC services using both passive logs and active measurements. These risks can be exploited by attackers, through observing only the traffic rates of HSCs, to infer the working state of cameras and even the daily activity routine in places where the camera is installed. Moreover, we find the premium users who pay an extra fee are especially vulnerable to such privacy inferences. We propose countermeasures from the perspectives of susceptible users and HSC providers to mitigate the risks.

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Under the Concealing Surface: Detecting and Understanding Live Webcams in the Wild

Cited by 10 publications

References 18 publications

Interaction Design of Wellness Building Space by Deep Learning and VR Technology in the Context of Internet of Things

Interaction Design of Wellness Building Space by Deep Learning and VR Technology in the Context of Internet of Things

GeoCAM: An IP-Based Geolocation Service Through Fine-Grained and Stable Webcam Landmarks

Characterising Usage Patterns and Privacy Risks of a Home Security Camera Service

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