Zobrazeno 1 - 10
of 516
pro vyhledávání: '"SUBRAHMANIAN, V. S."'
Although machine learning based algorithms have been extensively used for detecting phishing websites, there has been relatively little work on how adversaries may attack such "phishing detectors" (PDs for short). In this paper, we propose a set of G
Externí odkaz:
http://arxiv.org/abs/2212.05380
Detecting groups of people who are jointly deceptive in video conversations is crucial in settings such as meetings, sales pitches, and negotiations. Past work on deception in videos focuses on detecting a single deceiver and uses facial or visual fe
Externí odkaz:
http://arxiv.org/abs/2106.06163
Identifying persuasive speakers in an adversarial environment is a critical task. In a national election, politicians would like to have persuasive speakers campaign on their behalf. When a company faces adverse publicity, they would like to engage p
Externí odkaz:
http://arxiv.org/abs/2006.11405
Past work on evacuation planning assumes that evacuees will follow instructions -- however, there is ample evidence that this is not the case. While some people will follow instructions, others will follow their own desires. In this paper, we present
Externí odkaz:
http://arxiv.org/abs/2002.08114
We study the problem of predicting whether the price of the 21 most popular cryptocurrencies (according to coinmarketcap.com) will go up or down on day d, using data up to day d-1. Our C2P2 algorithm is the first algorithm to consider the fact that t
Externí odkaz:
http://arxiv.org/abs/1906.00564
Autor:
Bai, Chongyang, Bolonkin, Maksim, Burgoon, Judee, Chen, Chao, Dunbar, Norah, Singh, Bharat, Subrahmanian, V. S., Wu, Zhe
Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video. In this paper, we propose a new ADD framework whic
Externí odkaz:
http://arxiv.org/abs/1905.08617
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
We present a system for covert automated deception detection in real-life courtroom trial videos. We study the importance of different modalities like vision, audio and text for this task. On the vision side, our system uses classifiers trained on lo
Externí odkaz:
http://arxiv.org/abs/1712.04415