Incidente 806: translated-es-Criminal Group Uses AI Deepfake Technology to Steal Personal Data in Hangzhou, Zhejiang
Descripción: translated-es-A criminal group in China used AI face-swapping technology to bypass face recognition systems on major platforms, steal personal data, and sell it to fraud syndicates. The group generated convincing video simulations from static photos to breach accounts, reportedly earning 200,000 yuan. After an investigation by the Hangzhou Public Security Bureau, four suspects were arrested across the provinces of Anhui, Guizhou, and Zhejiang.
Entidades
Ver todas las entidadesAlleged: Unknown deepfake detection technology developers developed an AI system deployed by Hu Mouyun , Hu Mouliang , Zhang Mouguo y Wu Mouhao, which harmed Chinese citizens , Zhejiang citizens , Anhui citizens y Guizhou citizens.
Estadísticas de incidentes
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
Informes del Incidente
Cronología de Informes
Convierta una foto de rostro estática en una función de "reconocimiento facial", luego inicie sesión en las cuentas de otras personas para recopilar información personal y luego venda la información a bandas fraudulentas... Recientemente, e…
Variantes
Una "Variante" es un incidente de IA similar a un caso conocido—tiene los mismos causantes, daños y sistema de IA. En lugar de enumerarlo por separado, lo agrupamos bajo el primer incidente informado. A diferencia de otros incidentes, las variantes no necesitan haber sido informadas fuera de la AIID. Obtenga más información del trabajo de investigación.
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