Exploring the Vulnerabilities of Apple's FaceID

In this article, we delve into the recent incident where FaceID, Apple's facial recognition system, was reportedly compromised. This incident underscores the importance of robust security measures in AI systems, emphasizing the need for responsible AI governance and trustworthy AI models. Learn more about ensuring safe and secure AI with the HISPI Project Cerebellum TAIM—contributors can join us through JOIN US to contribute to our efforts in harm prevention.

Matched TAIM controls

Suggested mapping from embedding similarity (not a formal assessment). Browse all TAIM controls

MEASURE 2.10 — similarity 0.475, rank 1. TAIM detail and related incidents →
MAP 1.6 — similarity 0.472, rank 2. TAIM detail and related incidents →
MANAGE 4.3 — similarity 0.470, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/26

Data source

Incident data is from the AI Incident Database (AIID).

When citing the database as a whole, please use:

McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.

Pre-print on arXiv · Database snapshots & citation guide

We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.