Face recognition systems are susceptible to presentation
attacks such as printed photo attacks, replay attacks, and
3D mask attacks. These attacks, primarily studied in visible
spectrum, aim to obfuscate or impersonate a person’s identity.
This paper presents a unique multispectral video face
database for face presentation attack using latex and paper
masks. The proposed Multispectral Latex Mask based
Video Face Presentation Attack (MLFP) database contains
1350 videos in visible, near infrared, and thermal spectrums.
Since the database consists of videos of subjects
without any mask as well as wearing ten different masks,
the effect of identity concealment is analyzed in each spectrum
using face recognition algorithms. We also present the
performance of existing presentation attack detection algorithms
on the proposed MLFP database. It is observed that
the thermal imaging spectrum is most effective in detecting
face presentation attacks.
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This content will become publicly available on May 31, 2025
TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to Their Source Communities
Alas, coordinated hate attacks, or raids, are becoming increasingly common online. In a nutshell, these are perpetrated by a group of aggressors who organize and coordinate operations on a platform (e.g., 4chan) to target victims on another community (e.g., YouTube). In this paper, we focus on attributing raids to their source community, paving the way for moderation approaches that take the context (and potentially the motivation) of an attack into consideration.We present TUBERAIDER, an attribution system achieving over 75% accuracy in detecting and attributing coordinated hate attacks on YouTube videos. We instantiate it using links to YouTube videos shared on 4chan's /pol/ board, r/The_Donald, and 16 Incels-related subreddits. We use a peak detector to identify a rise in the comment activity of a YouTube video, which signals that an attack may be occurring. We then train a machine learning classifier based on the community language (i.e., TF-IDF scores of relevant keywords) to perform the attribution. We test TUBERAIDER in the wild and present a few case studies of actual aggression attacks identified by it to showcase its effectiveness.
more » « less- PAR ID:
- 10536265
- Publisher / Repository:
- AAAI
- Date Published:
- Journal Name:
- Proceedings of the International AAAI Conference on Web and Social Media
- Volume:
- 18
- ISSN:
- 2162-3449
- Page Range / eLocation ID:
- 1354 to 1366
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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