The Department of Computer Science Engineering is proud to announce that Dr Sriramulu Bojjagani has published a paper titled “Blockchain-Based Security Framework for Sharing Digital Images using Reversible Data Hiding and Encryption” in the journal Multimedia Tools and Applications (MTAP) having an impact factor of 2.757.

The paper is published in collaboration with D.R Denslin Brabin from the Department of Computer Science and Engineering, DMI College of Engineering, Tamil Nadu and Christo Ananth from the Department of Electronics and Communication Engineering, St. Mother Theresa Engineering College, Tamil Nadu.

Abstract of the Research

Security is an important issue in current and next-generation networks. Blockchain will be an appropriate technology for securely sharing information in next-generation networks. Digital images are the prime medium attacked by cyber attackers. In this paper, a blockchain-based security framework is proposed for sharing digital images in a multi-user environment. The proposed framework uses reversible data hiding and encryption as component techniques. A novel high-capacity reversible data hiding scheme is also proposed to protect digital images. Reversible data hiding in combination with encryption protects the confidentiality, integrity and authentication of digital images. In the proposed technique, the digital image is compressed first to create room for data hiding, then the user signature is embedded; afterwards, the whole image is encrypted. For compression, JPEG lossy compression is used to create high capacity. For encryption, any symmetric block cipher or stream cipher can be used. Experimental results show that the proposed blockchain-based framework provides high security and the proposed reversible data hiding scheme provides high capacity and image quality.

Fig 1: The process of encoding during reversible data hiding

Dr Sriramulu Bojjagani also intends to work on the development of block-chain based solutions to intelligent transport systems and on addressing the challenges of security issues involved in connected and autonomous vehicles.

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Human-like care is difficult to replicate. Due to the lack of a reliable vision-based fall detection AI, it is often more effective to assign a lot of manpower towards vision-based detections that have not been efficiently implemented.

Ms Inturi Anita Rani, Research Scholar in the Department of Computer Science Engineering, working with her supervisor, Dr V. M. Manikandan, has worked on a paper titled, “A Novel Vision-Based Fall Detection Scheme using Keypoints of Human-Skeleton with Long Short-term Memory Network” in the Arabian Journal for Science and Engineering published by Springer with an Impact Factor of 2.33.

Abstract of the research:

Humans are skilled at visually recognizing and classifying actions in videos, but it’s tough to automate this process. Human action detection in videos is useful in applications like automated surveillance, assisted living, human-computer interaction, content-based video retrieval, and video summarization. The ability to recognize atomic actions like “walking,” “bending,” and “falling” is critical for activity analysis when monitoring elderly people’s daily activities. Our paper presents a new promising solution for fall detection using vision-based approaches. In this approach, we analyse the human joint points which are the prime motion indicators. A set of keypoints of the subject are acquired by applying the AlphaPose pre-trained network. These keypoints are inferred to be the joint points of the subject. The acquired keypoints are processed through a framework of convolutional neural network (CNN) layers. Here, the spatial correlation of the keypoints is analysed. The long-term dependencies are then preserved with the help of long short-term memory (LSTM) architecture. Our system detects five types of falls and six types of daily living activities. We used the UP-FALL detection dataset for validating our fall detection system and achieved commendable results when compared to the state-of-the-art approaches. For comparison, we employed the OpenPose network for keypoint detection. It is inferred from the results that the AlphaPose network is more precise in keypoint detection.

About the research paper:

In this paper, the author proposes a vision-based system that is capable of detecting various types of falls accurately through video processing with the help of a machine learning approach.

Implementation of the research:

The proposed scheme can be used to monitor the activity of elderly people and if any unusual falls happen, the information can be shared with caretakers to ensure emergency services.

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