Video surveillance systems are widely deployed with large systems for use in strategic places such as home security, public transportation, banks, ATM centers, city centers, airports, and public roads, and play a vital role in protecting critical infrastructures. As various attacks are possible in these systems, identifying attacks and considering suitable security measures are essential. In this paper, we present a detailed review of existing and possible threats in video surveillance, CCTV, and IP-camera systems. This provides insight for the better identification of the security risks associated with the design and deployment of these systems and promotes further research in this emerging field. We also present countermeasures to prevent and protect the surveillance systems from various security attacks.

Today, enhancement in sensing technology enables the use of multiple sensors to track human motion/activity precisely. Tracking human motion has various applications, such as fitness training, healthcare, rehabilitation, human-computer interaction, virtual reality, and activity recognition. Therefore, the fusion of multiple sensors creates new opportunities to develop and improve an existing system. This paper proposes a pose-tracking system by fusing multiple three-dimensional (3D) light detection and ranging (lidar) and inertial measurement unit (IMU) sensors. The initial step estimates the human skeletal parameters proportional to the target user’s height by extracting the point cloud from lidars. Next, IMUs are used to capture the orientation of each skeleton segment and estimate the respective joint positions. In the final stage, the displacement drift in the position is corrected by fusing the data from both sensors in real time. The installation setup is relatively effortless, flexible for sensor locations, and delivers results comparable to the state-of-the-art pose-tracking system. We evaluated the proposed system regarding its accuracy in the user’s height estimation, full-body joint position estimation, and reconstruction of the 3D avatar. We used a publicly available dataset for the experimental evaluation wherever possible. The results reveal that the accuracy of height and the position estimation is well within an acceptable range of ±3–5 cm. The reconstruction of the motion based on the publicly available dataset and our data is precise and realistic.

Over the years, gaze input modality has been an easy and demanding human–computer interaction (HCI) method for various applications. The research of gaze-based interactive applications has advanced considerably, as HCIs are no longer constrained to traditional input devices. In this paper, we propose a novel immersive eye-gaze-guided camera (called GazeGuide) that can seamlessly control the movements of a camera mounted on an unmanned aerial vehicle (UAV) from the eye-gaze of a remote user. The video stream captured by the camera is fed into a head-mounted display (HMD) with a binocular eye tracker. The user’s eye-gaze is the sole input modality to maneuver the camera. A user study was conducted considering the static and moving targets of interest in a three-dimensional (3D) space to evaluate the proposed framework. GazeGuide was compared with a state-of-the-art input modality remote controller. The qualitative and quantitative results showed that the proposed GazeGuide performed significantly better than the remote controller.

The rapid integration of the Internet of Things (IoT) into healthcare systems has raised concerns about the confidentiality and integrity of patient data transmitted over these networks. Although traditional cryptographic solutions, such as Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES), are secure, they often introduce significant computational and energy overheads that are unsuitable for constrained Internet of Things (IoT) devices. This paper proposes a lightweight stego-crypto framework that combines ChaCha20 encryption and Elliptic Curve Cryptography (ECC) for efficient key management with a 2D Discrete Wavelet Transform (DWT)-based steganographic technique. The proposed model embeds encrypted diagnostic text within medical images, ensuring secure and imperceptible transmission of sensitive information. Extensive evaluations on grayscale medical images demonstrate superior performance across various metrics, including Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Bit Error Rate (BER), Correlation, and system-level metrics such as CPU, memory, and power usage. Robustness against common image perturbations such as Gaussian noise, salt-and-pepper noise, JPEG compression, blurring, rotation, and translation is assessed using Bit Error Rate (BER) and payload correlation. Compared to RSA-AES and other conventional stego-crypto systems, our approach achieves a better trade-off between security, imperceptibility, and computational efficiency, making it highly suitable for real-time healthcare applications over IoT networks.

The limited resources available for Smart Meter (SM) devices on large-scale Smart Grid (SG) networks impose several constraints on SMs authentication. Currently, available authentication schemes are not suitable for this type of network. In particular, factors such as power and memory consumption impact the protocol efficiency and the device lifetime. Furthermore, high computational complexity leads to scalability issues in real-world scenarios, wherein large SGs need to handle a huge number of requests coming at a high rate. In this paper, we propose a lightweight authentication protocol for Smart Grids (LAP-SG), a novel scheme accounting for real resource-constrained SM providing reduced computation power, memory requirements, communication overhead, and electricity consumption. We prove the security of LAP-SG using both informal security analysis and a formal security model. We further prove the security of LAP-SG by testing it using AVISPA and ProVerif tools, showing its security against all known attacks. To assess LAP-SG performance in a real-world scenario, we measure its performance using the configuration of the Atmel family of SM devices. When compared to the state of the art, LAP-SG attains three times Smaller computation cost, reduced communication costs (up to 400 bits), and nearly four times lower storage cost.

Acquisition of 3D point cloud data (PCD) using a laser scanner and aligning it with a video frame is a new approach that is efficient for retrofitting comprehensive objects in heavy pipeline industrial facilities. This work contributes a generic framework for interactive retrofitting in a virtual environment and an unmanned aerial vehicle (UAV)-based sensory setup design to acquire PCD. The framework adopts a 4-in-1 alignment using a point cloud registration algorithm for a pre-processed PCD alignment with the partial PCD, and frame-by-frame registration method for video alignment. This work also proposes a virtual interactive retrofitting framework that uses pre-defined 3D computer-aided design models (CAD) with a customized graphical user interface (GUI) and visualization of a 4-in-1 aligned video scene from a UAV camera in a desktop environment. Trials were carried out using the proposed framework in a real environment at a water treatment facility. A qualitative and quantitative study was conducted to evaluate the performance of the proposed generic framework from participants by adopting the appropriate questionnaire and retrofitting task-oriented experiment. Overall, it was found that the proposed framework could be a solution for interactive 3D CAD model retrofitting on a combination of UAV sensory setup-acquired PCD and real-time video from the camera in heavy industrial facilities.