4,070 research outputs found
Social Turing Tests: Crowdsourcing Sybil Detection
As popular tools for spreading spam and malware, Sybils (or fake accounts)
pose a serious threat to online communities such as Online Social Networks
(OSNs). Today, sophisticated attackers are creating realistic Sybils that
effectively befriend legitimate users, rendering most automated Sybil detection
techniques ineffective. In this paper, we explore the feasibility of a
crowdsourced Sybil detection system for OSNs. We conduct a large user study on
the ability of humans to detect today's Sybil accounts, using a large corpus of
ground-truth Sybil accounts from the Facebook and Renren networks. We analyze
detection accuracy by both "experts" and "turkers" under a variety of
conditions, and find that while turkers vary significantly in their
effectiveness, experts consistently produce near-optimal results. We use these
results to drive the design of a multi-tier crowdsourcing Sybil detection
system. Using our user study data, we show that this system is scalable, and
can be highly effective either as a standalone system or as a complementary
technique to current tools
A Response to Glaze Purification via IMPRESS
Recent work proposed a new mechanism to remove protective perturbation added
by Glaze in order to again enable mimicry of art styles from images protected
by Glaze. Despite promising results shown in the original paper, our own tests
with the authors' code demonstrated several limitations of the proposed
purification approach. The main limitations are 1) purification has a limited
effect when tested on artists that are not well-known historical artists
already embedded in original training data, 2) problems in evaluation metrics,
and 3) collateral damage on mimicry result for clean images. We believe these
limitations should be carefully considered in order to understand real world
usability of the purification attack
SoK: Anti-Facial Recognition Technology
The rapid adoption of facial recognition (FR) technology by both government
and commercial entities in recent years has raised concerns about civil
liberties and privacy. In response, a broad suite of so-called "anti-facial
recognition" (AFR) tools has been developed to help users avoid unwanted facial
recognition. The set of AFR tools proposed in the last few years is
wide-ranging and rapidly evolving, necessitating a step back to consider the
broader design space of AFR systems and long-term challenges. This paper aims
to fill that gap and provides the first comprehensive analysis of the AFR
research landscape. Using the operational stages of FR systems as a starting
point, we create a systematic framework for analyzing the benefits and
tradeoffs of different AFR approaches. We then consider both technical and
social challenges facing AFR tools and propose directions for future research
in this field.Comment: Camera-ready version for Oakland S&P 202
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