CCTV Forensics in the Big Data Era: Challenges and Approaches

Gomm, Richard; Brooks, Ryan; Choo, Kim‐Kwang Raymond; Le-Khac, Nhien-An; Hew, Kien Wooi

doi:10.1007/978-3-030-47131-6_6

Cited by 5 publications

(13 citation statements)

References 11 publications

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“…Our best detectors were built using 8,387 images that were manually reviewed and annotated to contain 10,419 CCTV camera instances, and achieve an accuracy of up to 98.7%. We believe our present work, CCTVCV, can enable more future CCTV forensics applications and address some of its imminent challenges [6]. For example, OSRM-CCTV [30] and CCTV-Exposure [30] systems, building over CCTVCV can be used to analyze both future predictions and past retrospects, if a particular navigation route went nearby any CCTV cameras.…”

Section: Visual Results Samplesmentioning

confidence: 99%

“…We believe our present work, CCTVCV, can enable more future CCTV forensics applications and address some of its imminent challenges [6]. For example, OSRM-CCTV [30] and CCTV-Exposure [30] systems, building over CCTVCV can be used to analyze both future predictions and past retrospects, if a particular navigation route went nearby any CCTV cameras.…”

Section: Applicationsmentioning

confidence: 99%

“…In terms of cybersecurity, CCTV cameras, digital video recorders (DVRs), and video surveillance systems have already been the target of numerous cyberattacks [4], and they were the main culprit behind the legendary and massive attack by the Mirai Internet of Things (IoT) botnet [5]. Both the CCTV forensics challenges [6], and the privacy risks [7]- [11], associated with CCTVs are also well known. There are several ways to overcome CCTV forensics challenges and mitigate the privacy risks of CCTV cameras, including their additional features, such as face recognition.…”

Section: Introductionmentioning

confidence: 99%

“…There are several ways to overcome CCTV forensics challenges and mitigate the privacy risks of CCTV cameras, including their additional features, such as face recognition. One way is to develop, provide and use appropriate high-tech tools both for CCTV forensics applications [6] and against the invasion of privacy by CCTV cameras [12]. Examples of such tools include CCTV-aware route planning and navigation as well as real-time early warning systems that alert users when their video input-equipped mobile and embedded devices (e.g., wearables, smartphones, and drones) enter the potential field of view of CCTV cameras.…”

Section: Introductionmentioning

confidence: 99%

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CCTVCV: Computer Vision model/dataset supporting CCTV forensics and privacy applications

Turtiainen

Costin

Hämäläinen

et al. 2022

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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The increased, widespread, unwarranted, and unaccountable use of Closed-Circuit TeleVision (CCTV) cameras globally has raised concerns about privacy risks for the last several decades. Recent technological advances implemented in CCTV cameras, such as Artificial Intelligence (AI)-based facial recognition and Internet of Things (IoT) connectivity, fuel further concerns among privacy advocates. Machine learning and computer vision automated solutions may prove necessary and efficient to assist CCTV forensics of various types.In this paper, we introduce and release the first and only computer vision models are compatible with Microsoft common object in context (MS COCO) and capable of accurately detecting CCTV and video surveillance cameras in street view, generic images, and video frames.Our best detectors were built using 8,387 images, which were manually reviewed and annotated to contain 10,419 CCTV camera instances, and achieved an accuracy rate of up to 98.7%. This work proves fundamental to a handful of present and future applications that we discuss, such as CCTV forensics, pro-active detection of CCTV cameras, providing CCTV-aware routing, navigation, and geolocation services, and estimating their prevalence and density globally and on geographic boundaries.

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Section: Visual Results Samplesmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CCTVCV: Computer Vision model/dataset supporting CCTV forensics and privacy applications

Turtiainen

Costin

Hämäläinen

et al. 2022

2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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“…Gomm et al [9] reverse-engineered a GANZ DVR to retrieve video footage from its AVTECH file system. They were able to recover video footage that the company's native software was unable to retrieve.…”

Section: Rel Ated Workmentioning

confidence: 99%

IoT forensics: Exploiting unexplored log records from the HIKVISION file system

Dragonas

Lambrinoudakis

Kotsis

2023

Journal of Forensic Sciences

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CCTV surveillance systems are IoT products that can be found almost everywhere. Their digital forensic analysis often plays a key role in solving crimes. However, it is common for these devices to use proprietary file systems, which frequently hinders a complete examination. HIKVISION is a well‐known manufacturer of such devices that typically ships its products with its proprietary file system. The HIKVISION file system has been analyzed before but that research has focused on the recovery of video footage. In this paper, the HIKVISION file system is being revisited regarding the log records it stores. More specifically, these log records are thoroughly examined to uncover both their structure and meaning. These unexplored pieces of evidence remain unexploited by major commercial forensic software, yet they can contain critical information for an investigation. To further assist digital forensic examiners with their analysis, a Python utility, namely the Hikvision Log Analyzer, was developed as part of this study that can automate part of the process.

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