Visible to the public Biblio

Filters: Keyword is YouTube  [Clear All Filters]
Plager, Trenton, Zhu, Ying, Blackmon, Douglas A..  2020.  Creating a VR Experience of Solitary Confinement. 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). :692—693.
The goal of this project is to create a realistic VR experience of solitary confinement and study its impact on users. Although there have been active debates and studies on this subject, very few people have personal experience of solitary confinement. Our first aim is to create such an experience in VR to raise the awareness of solitary confinement. We also want to conduct user studies to compare the VR solitary confinement experience with other types of media experiences, such as films or personal narrations. Finally, we want to study people’s sense of time in such a VR environment.
Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S..  2020.  Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :3204—3213.
AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for datasets of DeepFake videos. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process. We conduct a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celeb-DF.
Yu, L., Wang, Q., Barrineau, G., Oakley, J., Brooks, R. R., Wang, K. C..  2017.  TARN: A SDN-based traffic analysis resistant network architecture. 2017 12th International Conference on Malicious and Unwanted Software (MALWARE). :91–98.
Destination IP prefix-based routing protocols are core to Internet routing today. Internet autonomous systems (AS) possess fixed IP prefixes, while packets carry the intended destination AS's prefix in their headers, in clear text. As a result, network communications can be easily identified using IP addresses and become targets of a wide variety of attacks, such as DNS/IP filtering, distributed Denial-of-Service (DDoS) attacks, man-in-the-middle (MITM) attacks, etc. In this work, we explore an alternative network architecture that fundamentally removes such vulnerabilities by disassociating the relationship between IP prefixes and destination networks, and by allowing any end-to-end communication session to have dynamic, short-lived, and pseudo-random IP addresses drawn from a range of IP prefixes rather than one. The concept is seemingly impossible to realize in todays Internet. We demonstrate how this is doable today with three different strategies using software defined networking (SDN), and how this can be done at scale to transform the Internet addressing and routing paradigms with the novel concept of a distributed software defined Internet exchange (SDX). The solution works with both IPv4 and IPv6, whereas the latter provides higher degrees of IP addressing freedom. Prototypes based on Open vSwitches (OVS) have been implemented for experimentation across the PEERING BGP testbed. The SDX solution not only provides a technically sustainable pathway towards large-scale traffic analysis resistant network (TARN) support, it also unveils a new business model for customer-driven, customizable and trustable end-to-end network services.