Research into autonomous vehicles has focused on purpose-built vehicles with Lidar, camera, and radar systems. Many vehicles on the road today have sensors built into them to provide advanced driver assistance systems. In this paper we assess the ability of low-end automotive radar coupled with lightweight algorithms to perform scene segmentation. Results from a variety of scenes demonstrate the viability of this approach that complement existing autonomous driving systems.
KEYWORDS: Video compression, Video, Facial recognition systems, Feature extraction, Image restoration, Image compression, Video acceleration, Super resolution, Video surveillance, Convolution
Face in video recognition (FiVR) is widely used in video surveillance and video analytic. Various solutions have been proposed to improve the performance of face detection, frame selection and face recognition in FiVR systems. However, all these methods have a common inherent ceiling", which is defined by the source video's quality. One key factor causing face image quality loss is video compression standards. To address this challenge, in this paper, first, we analysis and quantify the effects of video compression on the FiVR performance; secondly, we propose to use deep learning based model to mitigate artifacts in compressed input video. We apply the image based convolutional auto-encoder (CAE) to extract the features of input face images and restore them towards less artifacts. From the experimental results, our approach can mitigate artifacts on face images extracted from compressed videos and improve the overall face recognition (FR) performance by as much as 50% in TPR (True Positive Rate) at the same FPR (False Positive Rate) value.
Many forms of malware and security breaches exist today. One type of breach downgrades a cryptographic program by employing a man-in-the-middle attack. In this work, we explore the utilization of hardware events in conjunction with machine learning algorithms to detect which version of OpenSSL is being run during the encryption process. This allows for the immediate detection of any unknown downgrade attacks in real time. Our experimental results indicated this detection method is both feasible and practical. When trained with normal TLS and SSL data, our classifier was able to detect which protocol was being used with 99.995% accuracy. After the scope of the hardware event recording was enlarged, the accuracy diminished greatly, but to 53.244%. Upon removal of TLS 1.1 from the data set, the accuracy returned to 99.905%.
Protecting data is a critical part of life in the modern world. The science of protecting data, known as cryptography,
makes use of secret keys to encrypt data in a format that is not easily decipherable. However, most modern cryptography
systems use passwords to perform user authentication. These passwords are a weak link in the security chain, as well as
a common point of attack on cryptography schemes. One alternative to password usage is biometrics: using a person’s
physical characteristics to verify who the person is and unlock the data correspondingly. This study provides a concrete
implementation of the Cambridge biometric cryptosystem. In addition, hardware acceleration has been performed on the
system in order to reduce system runtime and energy usage, which is compared with software-level code optimization.
The experiment takes place on a Xilinx Zynq-7000 All Programmable SoC. Software implementation is run on one of
the embedded ARM A9 cores while hardware implementation makes use of the programmable logic. This has resulted in
an algorithm with strong performance characteristics in both energy usage and runtime.
Conference Committee Involvement (2)
Disruptive Technologies in Information Sciences
17 April 2018 | Orlando, FL, United States
Disruptive Technologies in Sensors and Sensor Systems
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