Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications

Compute and memory demands of state-of-the-art deep learning methods are still a shortcoming that must be addressed to make them useful at IoT end-nodes. In particular, recent results depict a hopeful prospect for image processing using Convolutional Neural Netwoks, CNNs, but the gap between software and hardware implementations is already considerable for IoT and mobile edge computing applications due to their high power consumption. This proposal performs low-power and real time deep learning-based multiple object visual tracking implemented on an NVIDIA Jetson TX2 development kit. It includes a camera and wireless connection capability and it is battery powered for mobile and outdoor applications. A collection of representative sequences captured with the on-board camera, dETRUSC video dataset, is used to exemplify the performance of the proposed algorithm and to facilitate benchmarking. The results in terms of power consumption and frame rate demonstrate the feasibility of deep learning algorithms on embedded platforms although more effort to joint algorithm and hardware design of CNNs is needed.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

An enhanced fingerprint template protection scheme

Fingerprint template protection (FTP) is required to secure authentication due to fingerprint has been widely used for user authentication systems. Fingerprint authentication consists of a microcontroller, fingerprint sensor, secure access control, and human interface. However, as many users frequently assess the systems, fingerprints could be replicated and modified by attackers. Currently, most existing FTP schemes fail to meet the properties of fingerprint authentication systems, namely diversity, revocability, security, and match/recognition performance, due to intra-user variability in fingerprint identifiers and matching issues in unencrypted domains. Therefore, this study aims to enhance the existing schemes by using chaos-based encryption and hash functions to meet the specified properties by securing users’ fingerprint templates (FT) within the embedded systems. Furthermore, an improved chaos-based encryption algorithm was proposed for encrypting FT. The MATLAB simulation with Fingerprint Verification Competition (FVC) 2002 database was used to measure the encryption results, secret key spaces, key sensitivity, histogram, correlation, differential, entropy information, matching/recognition analysis, and revocability. The proposed FTP scheme was also evaluated using Burrows–Abadi– Needham (BAN) logic analysis for protocol robustness with resistance to replay attacks, stolen-verifier attacks, and perfect forward secrecy. The results demonstrate that the enhanced chaos-based encryption algorithm for FTP improves its encryption time, which is 0.24 seconds faster than the selected benchmark study. The enhanced FTP scheme also achieved security, revocability, diversity, and matching/recognition performance properties. The matching/recognition performance evaluation produced higher verification rates and a low false rejection rate. The rates were 99.10 % and 0.90%, respectively. The equal error rate decreased from 2.10% to 1.05%. As a conclusion, the enhanced FTP scheme could be an alternative to the existing FTP for embedded system authentication to withstand various possible attacks and provides the desired security features. The scheme also can be a reference to comprehensive security analysis