Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Greenstadt, Rachel"'
Content moderation practices and technologies need to change over time as requirements and community expectations shift. However, attempts to restructure existing moderation practices can be difficult, especially for platforms that rely on their comm
Externí odkaz:
http://arxiv.org/abs/2402.17880
Autor:
Mehta, Pulak, Jagatap, Gauri, Gallagher, Kevin, Timmerman, Brian, Deb, Progga, Garg, Siddharth, Greenstadt, Rachel, Dolan-Gavitt, Brendan
Recent advancements in machine learning and computer vision have led to the proliferation of Deepfakes. As technology democratizes over time, there is an increasing fear that novice users can create Deepfakes, to discredit others and undermine public
Externí odkaz:
http://arxiv.org/abs/2304.14576
Autor:
Ballard, Cameron, Goldstein, Ian, Mehta, Pulak, Smothers, Genesis, Take, Kejsi, Zhong, Victoria, Greenstadt, Rachel, Lauinger, Tobias, McCoy, Damon
Publikováno v:
WWW 2022 Proceedings of the ACM Web Conference, April 2022, Pages 2707-2718
Conspiracy theories are increasingly a subject of research interest as society grapples with their rapid growth in areas such as politics or public health. Previous work has established YouTube as one of the most popular sites for people to host and
Externí odkaz:
http://arxiv.org/abs/2205.15943
Many online communities rely on postpublication moderation where contributors, even those that are perceived as being risky, are allowed to publish material immediately and where moderation takes place after the fact. An alternative arrangement invol
Externí odkaz:
http://arxiv.org/abs/2202.05548
The Tools and Tactics Used in Intimate Partner Surveillance: An Analysis of Online Infidelity Forums
Autor:
Tseng, Emily, Bellini, Rosanna, McDonald, Nora, Danos, Matan, Greenstadt, Rachel, McCoy, Damon, Dell, Nicola, Ristenpart, Thomas
Abusers increasingly use spyware apps, account compromise, and social engineering to surveil their intimate partners, causing substantial harms that can culminate in violence. This form of privacy violation, termed intimate partner surveillance (IPS)
Externí odkaz:
http://arxiv.org/abs/2005.14341
Numerous recent studies have demonstrated how Deep Neural Network (DNN) classifiers can be fooled by adversarial examples, in which an attacker adds perturbations to an original sample, causing the classifier to misclassify the sample. Adversarial at
Externí odkaz:
http://arxiv.org/abs/2001.11137
Autor:
Champion, Kaylea, McDonald, Nora, Bankes, Stephanie, Zhang, Joseph, Greenstadt, Rachel, Forte, Andrea, Hill, Benjamin Mako
Publikováno v:
Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 53 (November 2019)
By choice or by necessity, some contributors to commons-based peer production sites use privacy-protecting services to remain anonymous. As anonymity seekers, users of the Tor network have been cast both as ill-intentioned vandals and as vulnerable p
Externí odkaz:
http://arxiv.org/abs/1909.07929
User-generated content sites routinely block contributions from users of privacy-enhancing proxies like Tor because of a perception that proxies are a source of vandalism, spam, and abuse. Although these blocks might be effective, collateral damage i
Externí odkaz:
http://arxiv.org/abs/1904.04324
Underground forums where users discuss, buy, and sell illicit services and goods facilitate a better understanding of the economy and organization of cybercriminals. Prior work has shown that in particular private interactions provide a wealth of inf
Externí odkaz:
http://arxiv.org/abs/1805.04494
Recent studies have shown that Tor onion (hidden) service websites are particularly vulnerable to website fingerprinting attacks due to their limited number and sensitive nature. In this work we present a multi-level feature analysis of onion site fi
Externí odkaz:
http://arxiv.org/abs/1708.08475