34 citations · 58 across the 8 of their papers we have counts for
9 papers
Measuring Online Hate on 4chan using Pre-trained Deep Learning Models
Adrian Bermudez-Villalva, Maryam Mehrnezhad, Ehsan Toreini
Online hate speech can harmfully impact individuals and groups, specifically on non-moderated platforms such as 4chan where users can post anonymous content. This work focuses on a…
Perceptual Hash Inversion Attacks on Image-Based Sexual Abuse Removal Tools
Sophie Hawkes, Christian Weinert, Teresa Almeida +1
We show that perceptual hashing, crucial for detecting and removing image-based sexual abuse (IBSA) online, faces vulnerabilities from low-budget inversion attacks based on generat…
The Importance of Collective Privacy in Digital Sexual and Reproductive Health
Teresa Almeida, Maryam Mehrnezhad, Stephen Cook
There is an abundance of digital sexual and reproductive health technologies that presents a concern regarding their potential sensitive data breaches. We analyzed 15 Internet of T…
Verifiable Fairness: Privacy-preserving Computation of Fairness for Machine Learning Systems
Ehsan Toreini, Maryam Mehrnezhad, Aad van Moorsel
Fair machine learning is a thriving and vibrant research topic. In this paper, we propose Fairness as a Service (FaaS), a secure, verifiable and privacy-preserving protocol to comp…
A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards
Joshua Harrison, Ehsan Toreini, Maryam Mehrnezhad
With recent developments in deep learning, the ubiquity of micro-phones and the rise in online services via personal devices, acoustic side channel attacks present a greater threat…
Invisible, Unreadable, and Inaudible Cookie Notices: An Evaluation of Cookie Notices for Users with Visual Impairments
James M. Clarke, Maryam Mehrnezhad, Ehsan Toreini
This paper investigates the accessibility of cookie notices on websites for users with visual impairments (VI) via a set of system studies on top UK websites (n=46) and a user stud…