Visible to the public Biblio

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El-Din Abd El-Raouf, Karim Alaa, Bahaa-Eldin, Ayman M., Sobh, Mohamed A..  2019.  Multipath Traffic Engineering for Software Defined Networking. 2019 14th International Conference on Computer Engineering and Systems (ICCES). :132—136.
ASA systems (firewall, IDS, IPS) are probable to become communication bottlenecks in networks with growing network bandwidths. To alleviate this issue, we suggest to use Application-aware mechanism based on Deep Packet Inspection (DPI) to bypass chosen traffic around firewalls. The services of Internet video sharing gained importance and expanded their share of the multimedia market. The Internet video should meet strict service quality (QoS) criteria to make the broadcasting of broadcast television a viable and comparable level of quality. However, since the Internet video relies on packet communication, it is subject to delays, transmission failures, loss of data and bandwidth restrictions that may have a catastrophic effect on the quality of multimedia.
Adari, Suman Kalyan, Garcia, Washington, Butler, Kevin.  2019.  Adversarial Video Captioning. 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). :24—27.
In recent years, developments in the field of computer vision have allowed deep learning-based techniques to surpass human-level performance. However, these advances have also culminated in the advent of adversarial machine learning techniques, capable of launching targeted image captioning attacks that easily fool deep learning models. Although attacks in the image domain are well studied, little work has been done in the video domain. In this paper, we show it is possible to extend prior attacks in the image domain to the video captioning task, without heavily affecting the video's playback quality. We demonstrate our attack against a state-of-the-art video captioning model, by extending a prior image captioning attack known as Show and Fool. To the best of our knowledge, this is the first successful method for targeted attacks against a video captioning model, which is able to inject 'subliminal' perturbations into the video stream, and force the model to output a chosen caption with up to 0.981 cosine similarity, achieving near-perfect similarity to chosen target captions.
Khelifi, Hakima, Luo, Senlin, Nour, Boubakr, Moungla, Hassine.  2019.  A QoS-Aware Cache Replacement Policy for Vehicular Named Data Networks. 2019 IEEE Global Communications Conference (GLOBECOM). :1—6.

Vehicular Named Data Network (VNDN) uses Named Data Network (NDN) as a communication enabler. The communication is achieved using the content name instead of the host address. NDN integrates content caching at the network level rather than the application level. Hence, the network becomes aware of content caching and delivering. The content caching is a fundamental element in VNDN communication. However, due to the limitations of the cache store, only the most used content should be cached while the less used should be evicted. Traditional caching replacement policies may not work efficiently in VNDN due to the large and diverse exchanged content. To solve this issue, we propose an efficient cache replacement policy that takes the quality of service into consideration. The idea consists of classifying the traffic into different classes, and split the cache store into a set of sub-cache stores according to the defined traffic classes with different storage capacities according to the network requirements. Each content is assigned a popularity-density value that balances the content popularity with its size. Content with the highest popularity-density value is cached while the lowest is evicted. Simulation results prove the efficiency of the proposed solution to enhance the overall network quality of service.

Fleck, Daniel, Stavrou, Angelos, Kesidis, George, Nasiriani, Neda, Shan, Yuquan, Konstantopoulos, Takis.  2018.  Moving-Target Defense Against Botnet Reconnaissance and an Adversarial Coupon-Collection Model. 2018 IEEE Conference on Dependable and Secure Computing (DSC). :1—8.

We consider a cloud based multiserver system consisting of a set of replica application servers behind a set of proxy (indirection) servers which interact directly with clients over the Internet. We study a proactive moving-target defense to thwart a DDoS attacker's reconnaissance phase and consequently reduce the attack's impact. The defense is effectively a moving-target (motag) technique in which the proxies dynamically change. The system is evaluated using an AWS prototype of HTTP redirection and by numerical evaluations of an “adversarial” coupon-collector mathematical model, the latter allowing larger-scale extrapolations.

Jeong, Yena, Hwang, DongYeop, Kim, Ki-Hyung.  2019.  Blockchain-Based Management of Video Surveillance Systems. 2019 International Conference on Information Networking (ICOIN). :465–468.
In this paper, we propose a video surveillance system based on blockchain system. The proposed system consists of a blockchain network with trusted internal managers. The metadata of the video is recorded on the distributed ledger of the blockchain, thereby blocking the possibility of forgery of the data. The proposed architecture encrypts and stores the video, creates a license within the blockchain, and exports the video. Since the decryption key for the video is managed by the private DB of the blockchain, it is not leaked by the internal manager unauthorizedly. In addition, the internal administrator can manage and export videos safely by exporting the license generated in the blockchain to the DRM-applied video player.
liu, Shidong, Bu, Xiande.  2019.  Performance Modeling and Assessment of Unified Video Surveillance System Based on Ubiquitous SG-eIoT. 2019 IEEE International Conference on Energy Internet (ICEI). :238–243.
Video surveillance system is an important application system on the ubiquitous SG-eIoT. A comparative analysis of the traditional video surveillance scheme and the unified video surveillance solution in the eIoT environment is made. Network load and service latency parameters under the two schemes are theoretically modeled and simulated. Combined with the simulation results, the corresponding suggestions for the access of video terminals in the ubiquitous eIoT are given.
Wang, Yongtao.  2019.  Development of AtoN Real-time Video Surveillance System Based on the AIS Collision Warning. 2019 5th International Conference on Transportation Information and Safety (ICTIS). :393–398.
In view of the challenges with Aids to Navigation (AtoN) managements and emergency response, the present study designs and presents an AtoN real-time video surveillance system based on the AIS collision warning. The key technologies regarding with AtoN cradle head control and testing algorithms, video image fusion, system operation and implementation are demonstrated in details. Case study is performed at Guan River (China) to verify the effectiveness of the AtoN real-time video surveillance system for maritime security supervision. The research results indicate that the intellective level of the AtoN maintenance and managements could be significantly improved. The idea of designing modules brings a good flexibility and a high portability for the present surveillance system, therefore provides a guidance for the design of similar maritime surveillance systems.
Kim, Dongchil, Kim, Kyoungman, Park, Sungjoo.  2019.  Automatic PTZ Camera Control Based on Deep-Q Network in Video Surveillance System. 2019 International Conference on Electronics, Information, and Communication (ICEIC). :1–3.
Recently, Pan/Tilt/Zoom (PTZ) camera has been widely used in video surveillance systems. However, it is difficult to automatically control PTZ cameras according to moving objects in the surveillance area. This paper proposes an automatic camera control method based on a Deep-Q Network (DQN) for improving the recognition accuracy of anomaly actions in the video surveillance system. To generate PTZ camera control values, the proposed method uses the position and size information of the object which received from the video analysis system. Through implementation results, the proposed method can automatically control the PTZ camera according to moving objects.
Kalan, Reza Shokri, Sayit, Muge, Clayman, Stuart.  2019.  Optimal Cache Placement and Migration for Improving the Performance of Virtualized SAND. 2019 IEEE Conference on Network Softwarization (NetSoft). :78–83.

Nowadays, video streaming over HTTP is one of the most dominant Internet applications, using adaptive video techniques. Network assisted approaches have been proposed and are being standardized in order to provide high QoE for the end-users of such applications. SAND is a recent MPEG standard where DASH Aware Network Elements (DANEs) are introduced for this purpose. As web-caches are one of the main components of the SAND architecture, the location and the connectivity of these web-caches plays an important role in the user's QoE. The nature of SAND and DANE provides a good foundation for software controlled virtualized DASH environments, and in this paper, we propose a cache location algorithm and a cache migration algorithm for virtualized SAND deployments. The optimal locations for the virtualized DANEs is determined by an SDN controller and migrates it based on gathered statistics. The performance of the resulting system shows that, when SDN and NFV technologies are leveraged in such systems, software controlled virtualized approaches can provide an increase in QoE.

Zhou, Yiwen, Shen, Qili, Dong, Mianxiong, Ota, Kaoru, Wu, Jun.  2019.  Chaos-Based Delay-Constrained Green Security Communications for Fog-Enabled Information-Centric Multimedia Network. 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring). :1–6.
The Information-Centric Network possessing the content-centric features, is the innovative architecture of the next generation of network. Collaborating with fog computing characterized by its strong edge power, ICN will become the development trend of the future network. The emergence of Information-Centric Multimedia Network (ICMN) can meet the increasing demand for transmission of multimedia streams in the current Internet environment. The data transmission has become more delay-constrained and convenient because of the distributed storage, the separation between the location of information and terminals, and the strong cacheability of each node in ICN. However, at the same time, the security of the multimedia streams in the delivery process still requires further protection against wiretapping, interception or attacking. In this paper, we propose the delay-constrained green security communications for ICMN based on chaotic encryption and fog computing so as to transmit multimedia streams in a more secure and time-saving way. We adapt a chaotic cryptographic method to ICMN, implementing the encryption and decryption of multimedia streams. Meanwhile, the network edge capability to process the encryption and decryption is enhanced. Thanks to the fog computing, the strengthened transmission speed of the multimedia streams can fulfill the need for short latency. The work in the paper is of great significance to improve the green security communications of multimedia streams in ICMN.
DiPaola, Steve, Yalçin, Özge Nilay.  2019.  A multi-layer artificial intelligence and sensing based affective conversational embodied agent. 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). :91–92.
Building natural and conversational virtual humans is a task of formidable complexity. We believe that, especially when building agents that affectively interact with biological humans in real-time, a cognitive science-based, multilayered sensing and artificial intelligence (AI) systems approach is needed. For this demo, we show a working version (through human interaction with it) our modular system of natural, conversation 3D virtual human using AI or sensing layers. These including sensing the human user via facial emotion recognition, voice stress, semantic meaning of the words, eye gaze, heart rate, and galvanic skin response. These inputs are combined with AI sensing and recognition of the environment using deep learning natural language captioning or dense captioning. These are all processed by our AI avatar system allowing for an affective and empathetic conversation using an NLP topic-based dialogue capable of using facial expressions, gestures, breath, eye gaze and voice language-based two-way back and forth conversations with a sensed human. Our lab has been building these systems in stages over the years.
Eetha, S., Agrawal, S., Neelam, S..  2018.  Zynq FPGA Based System Design for Video Surveillance with Sobel Edge Detection. 2018 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS). :76–79.

Advancements in semiconductor domain gave way to realize numerous applications in Video Surveillance using Computer vision and Deep learning, Video Surveillances in Industrial automation, Security, ADAS, Live traffic analysis etc. through image understanding improves efficiency. Image understanding requires input data with high precision which is dependent on Image resolution and location of camera. The data of interest can be thermal image or live feed coming for various sensors. Composite(CVBS) is a popular video interface capable of streaming upto HD(1920x1080) quality. Unlike high speed serial interfaces like HDMI/MIPI CSI, Analog composite video interface is a single wire standard supporting longer distances. Image understanding requires edge detection and classification for further processing. Sobel filter is one the most used edge detection filter which can be embedded into live stream. This paper proposes Zynq FPGA based system design for video surveillance with Sobel edge detection, where the input Composite video decoded (Analog CVBS input to YCbCr digital output), processed in HW and streamed to HDMI display simultaneously storing in SD memory for later processing. The HW design is scalable for resolutions from VGA to Full HD for 60fps and 4K for 24fps. The system is built on Xilinx ZC702 platform and TVP5146 to showcase the functional path.

Liu, Y., Yang, Y., Shi, A., Jigang, P., Haowei, L..  2019.  Intelligent monitoring of indoor surveillance video based on deep learning. 2019 21st International Conference on Advanced Communication Technology (ICACT). :648–653.

With the rapid development of information technology, video surveillance system has become a key part in the security and protection system of modern cities. Especially in prisons, surveillance cameras could be found almost everywhere. However, with the continuous expansion of the surveillance network, surveillance cameras not only bring convenience, but also produce a massive amount of monitoring data, which poses huge challenges to storage, analytics and retrieval. The smart monitoring system equipped with intelligent video analytics technology can monitor as well as pre-alarm abnormal events or behaviours, which is a hot research direction in the field of surveillance. This paper combines deep learning methods, using the state-of-the-art framework for instance segmentation, called Mask R-CNN, to train the fine-tuning network on our datasets, which can efficiently detect objects in a video image while simultaneously generating a high-quality segmentation mask for each instance. The experiment show that our network is simple to train and easy to generalize to other datasets, and the mask average precision is nearly up to 98.5% on our own datasets.

Sen, N., Dantu, R., Vempati, J., Thompson, M..  2018.  Performance Analysis of Elliptic Curves for Real-Time Video Encryption. 2018 National Cyber Summit (NCS). :64-71.

The use of real-time video streaming is increasing day-by-day, and its security has become a serious issue now. Video encryption is a challenging task because of its large frame size. Video encryption can be done with symmetric key as well as asymmetric key encryption. Among different asymmetric key encryption technique, ECC performs better than other algorithms like RSA in terms of smaller key size and faster encryption and decryption operation. In this work, we have analyzed the performance of 18 different ECC curves and suggested some suitable curves for real-time video encryption.

Aloui, M., Elbiaze, H., Glitho, R., Yangui, S..  2018.  Analytics as a service architecture for cloud-based CDN: Case of video popularity prediction. 2018 15th IEEE Annual Consumer Communications Networking Conference (CCNC). :1–4.
User Generated Videos (UGV) are the dominating content stored in scattered caches to meet end-user Content Delivery Networks (CDN) requests with quality of service. End-User behaviour leads to a highly variable UGV popularity. This aspect can be exploited to efficiently utilize the limited storage of the caches, and improve the hit ratio of UGVs. In this paper, we propose a new architecture for Data Analytics in Cloud-based CDN to derive UGVs popularity online. This architecture uses RESTful web services to gather CDN logs, store them through generic collections in a NoSQL database, and calculate related popular UGVs in a real time fashion. It uses a dynamic model training and prediction services to provide each CDN with related popular videos to be cached based on the latest trained model. The proposed architecture is implemented with k-means clustering prediction model and the obtained results are 99.8% accurate.
Khan, F., Quweider, M., Torres, M., Goldsmith, C., Lei, H., Zhang, L..  2018.  Block Level Streaming Based Alternative Approach for Serving a Large Number of Workstations Securely and Uniformly. 2018 1st International Conference on Data Intelligence and Security (ICDIS). :92–98.
There are different traditional approaches to handling a large number of computers or workstations in a campus setting, ranging from imaging to virtualized environments. The common factor among the traditional approaches is to have a user workstation with a local hard drive (nonvolatile storage), scratchpad volatile memory, a CPU (Central Processing Unit) and connectivity to access resources on the network. This paper presents the use of block streaming, normally used for storage, to serve operating system and applications on-demand over the network to a workstation, also referred to as a client, a client computer, or a client workstation. In order to avoid per seat licensing, an Open Source solution is used, and in order to minimize the field maintenance and meet security privacy constraints, a workstation need not have a permanent storage such as a hard disk drive. A complete blue print, based on performance analyses, is provided to determine the type of network architecture, servers, workstations per server, and minimum workstation configuration, suitable for supporting such a solution. The results of implementing the proposed solution campus wide, supporting more than 450 workstations, are presented as well.
Schonherr, L., Zeiler, S., Kolossa, D..  2017.  Spoofing detection via simultaneous verification of audio-visual synchronicity and transcription. 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). :591–598.

Acoustic speaker recognition systems are very vulnerable to spoofing attacks via replayed or synthesized utterances. One possible countermeasure is audio-visual speaker recognition. Nevertheless, the addition of the visual stream alone does not prevent spoofing attacks completely and only provides further information to assess the authenticity of the utterance. Many systems consider audio and video modalities independently and can easily be spoofed by imitating only a single modality or by a bimodal replay attack with a victim's photograph or video. Therefore, we propose the simultaneous verification of the data synchronicity and the transcription in a challenge-response setup. We use coupled hidden Markov models (CHMMs) for a text-dependent spoofing detection and introduce new features that provide information about the transcriptions of the utterance and the synchronicity of both streams. We evaluate the features for various spoofing scenarios and show that the combination of the features leads to a more robust recognition, also in comparison to the baseline method. Additionally, by evaluating the data on unseen speakers, we show the spoofing detection to be applicable in speaker-independent use-cases.

Langfinger, M., Schneider, M., Stricker, D., Schotten, H. D..  2017.  Addressing Security Challenges in Industrial Augmented Reality Systems. 2017 IEEE 15th International Conference on Industrial Informatics (INDIN). :299–304.

In context of Industry 4.0 Augmented Reality (AR) is frequently mentioned as the upcoming interface technology for human-machine communication and collaboration. Many prototypes have already arisen in both the consumer market and in the industrial sector. According to numerous experts it will take only few years until AR will reach the maturity level to be deployed in productive applications. Especially for industrial usage it is required to assess security risks and challenges this new technology implicates. Thereby we focus on plant operators, Original Equipment Manufacturers (OEMs) and component vendors as stakeholders. Starting from several industrial AR use cases and the structure of contemporary AR applications, in this paper we identify security assets worthy of protection and derive the corresponding security goals. Afterwards we elaborate the threats industrial AR applications are exposed to and develop an edge computing architecture for future AR applications which encompasses various measures to reduce security risks for our stakeholders.

Xie, D., Wang, Y..  2017.  High definition wide dynamic video surveillance system based on FPGA. 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). :2403–2407.

A high definition(HD) wide dynamic video surveillance system is designed and implemented based on Field Programmable Gate Array(FPGA). This system is composed of three subsystems, which are video capture, video wide dynamic processing and video display subsystem. The images in the video are captured directly through the camera that is configured in a pattern have long exposure in odd frames and short exposure in even frames. The video data stream is buffered in DDR2 SDRAM to obtain two adjacent frames. Later, the image data fusion is completed by fusing the long exposure image with the short exposure image (pixel by pixel). The video image display subsystem can display the image through a HDMI interface. The system is designed on the platform of Lattice ECP3-70EA FPGA, and camera is the Panasonic MN34229 sensor. The experimental result shows that this system can expand dynamic range of the HD video with 30 frames per second and a resolution equal to 1920*1080 pixels by real-time wide dynamic range (WDR) video processing, and has a high practical value.

Maheshwari, B. C., Burns, J., Blott, M., Gambardella, G..  2017.  Implementation of a scalable real time canny edge detector on programmable SOC. 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA). :1–5.

In today's world, we are surrounded by variety of computer vision applications e.g. medical imaging, bio-metrics, security, surveillance and robotics. Most of these applications require real time processing of a single image or sequence of images. This real time image/video processing requires high computational power and specialized hardware architecture and can't be achieved using general purpose CPUs. In this paper, a FPGA based generic canny edge detector is introduced. Edge detection is one of the basic steps in image processing, image analysis, image pattern recognition, and computer vision. We have implemented a re-sizable canny edge detector IP on programmable logic (PL) of PYNQ-Platform. The IP is integrated with HDMI input/output blocks and can process 1080p input video stream at 60 frames per second. As mentioned the canny edge detection IP is scalable with respect to frame size i.e. depending on the input frame size, the hardware architecture can be scaled up or down by changing the template parameters. The offloading of canny edge detection from PS to PL causes the CPU usage to drop from about 100% to 0%. Moreover, hardware based edge detector runs about 14 times faster than the software based edge detector running on Cortex-A9 ARM processor.

Kotel, S., Sbiaa, F., Zeghid, M., Machhout, M., Baganne, A., Tourki, R..  2016.  Efficient Hybrid Encryption System Based on Block Cipher and Chaos Generator. 2016 IEEE International Conference on Computer and Information Technology (CIT). :375–382.

In recent years, more and more multimedia data are generated and transmitted in various fields. So, many encryption methods for multimedia content have been put forward to satisfy various applications. However, there are still some open issues. Each encryption method has its advantages and drawbacks. Our main goal is expected to provide a solution for multimedia encryption which satisfies the target application constraints and performs metrics of the encryption algorithm. The Advanced Encryption Standard (AES) is the most popular algorithm used in symmetric key cryptography. Furthermore, chaotic encryption is a new research direction of cryptography which is characterized by high initial-value sensitivity and good randomness. In this paper we propose a hybrid video cryptosystem which combines two encryption techniques. The proposed cryptosystem realizes the video encryption through the chaos and AES in CTR mode. Experimental results and security analysis demonstrate that this cryptosystem is highly efficient and a robust system for video encryption.

Yap, B. L., Baskaran, V. M..  2016.  Active surveillance using depth sensing technology \#8212; Part I: Intrusion detection. 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). :1–2.

In part I of a three-part series on active surveillance using depth-sensing technology, this paper proposes an algorithm to identify outdoor intrusion activities by monitoring skeletal positions from Microsoft Kinect sensor in real-time. This algorithm implements three techniques to identify a premise intrusion. The first technique observes a boundary line along the wall (or fence) of a surveilled premise for skeletal trespassing detection. The second technique observes the duration of a skeletal object within a region of a surveilled premise for loitering detection. The third technique analyzes the differences in skeletal height to identify wall climbing. Experiment results suggest that the proposed algorithm is able to detect trespassing, loitering and wall climbing at a rate of 70%, 85% and 80% respectively.

Li, H., He, Y., Sun, L., Cheng, X., Yu, J..  2016.  Side-channel information leakage of encrypted video stream in video surveillance systems. IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications. :1–9.

Video surveillance has been widely adopted to ensure home security in recent years. Most video encoding standards such as H.264 and MPEG-4 compress the temporal redundancy in a video stream using difference coding, which only encodes the residual image between a frame and its reference frame. Difference coding can efficiently compress a video stream, but it causes side-channel information leakage even though the video stream is encrypted, as reported in this paper. Particularly, we observe that the traffic patterns of an encrypted video stream are different when a user conducts different basic activities of daily living, which must be kept private from third parties as obliged by HIPAA regulations. We also observe that by exploiting this side-channel information leakage, attackers can readily infer a user's basic activities of daily living based on only the traffic size data of an encrypted video stream. We validate such an attack using two off-the-shelf cameras, and the results indicate that the user's basic activities of daily living can be recognized with a high accuracy.

Shahrak, M. Z., Ye, M., Swaminathan, V., Wei, S..  2016.  Two-way real time multimedia stream authentication using physical unclonable functions. 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP). :1–4.

Multimedia authentication is an integral part of multimedia signal processing in many real-time and security sensitive applications, such as video surveillance. In such applications, a full-fledged video digital rights management (DRM) mechanism is not applicable due to the real time requirement and the difficulties in incorporating complicated license/key management strategies. This paper investigates the potential of multimedia authentication from a brand new angle by employing hardware-based security primitives, such as physical unclonable functions (PUFs). We show that the hardware security approach is not only capable of accomplishing the authentication for both the hardware device and the multimedia stream but, more importantly, introduce minimum performance, resource, and power overhead. We justify our approach using a prototype PUF implementation on Xilinx FPGA boards. Our experimental results on the real hardware demonstrate the high security and low overhead in multimedia authentication obtained by using hardware security approaches.

Mukherjee, M., Edwards, J., Kwon, H., Porta, T. F. L..  2015.  Quality of information-aware real-time traffic flow analysis and reporting. 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). :69–74.

In this paper we present a framework for Quality of Information (QoI)-aware networking. QoI quantifies how useful a piece of information is for a given query or application. Herein, we present a general QoI model, as well as a specific example instantiation that carries throughout the rest of the paper. In this model, we focus on the tradeoffs between precision and accuracy. As a motivating example, we look at traffic video analysis. We present simple algorithms for deriving various traffic metrics from video, such as vehicle count and average speed. We implement these algorithms both on a desktop workstation and less-capable mobile device. We then show how QoI-awareness enables end devices to make intelligent decisions about how to process queries and form responses, such that huge bandwidth savings are realized.