Description: Authorities in Johor, Malaysia reportedly arrested a 16-year-old student who allegedly used AI-enabled tools to generate and sell deepfake images depicting schoolmates and former students. According to police, the images were created from social media photos and distributed online for payment. The alleged activity affected dozens of victims, including minors, some of whom may have been unaware of the misuse.
Entities
View all entitiesAlleged: Image generator developers and Deepfake technology developers developed an AI system deployed by Unnamed student from Johor , Students , Boys and high school students, which harmed Students , high school students , Unnamed students from Johor , Minors , Epistemic integrity and Educational communities.
Alleged implicated AI systems: Deepfake technology and Image generation technology
Incident Stats
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
4.3. Fraud, scams, and targeted manipulation
Risk Domain
The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
- Malicious Actors & Misuse
Entity
Which, if any, entity is presented as the main cause of the risk
Human
Timing
The stage in the AI lifecycle at which the risk is presented as occurring
Post-deployment
Intent
Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
Intentional
Incident Reports
Reports Timeline
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JOHOR BAHRU - A 16-year-old boy has been arrested for allegedly using artificial intelligence (AI) to create pornographic images of his schoolmates and school alumni.
Johor police chief M. Kumar said the cops had received eight reports agai…
Variants
A "variant" is an AI incident similar to a known case—it has the same causes, harms, and AI system. Instead of listing it separately, we group it under the first reported incident. Unlike other incidents, variants do not need to have been reported outside the AIID. Learn more from the research paper.
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