Descripción: Scammers from Nigeria, known as "Yahoo Boys," are reportedly utilizing real-time deepfake technology to impersonate individuals during video calls, deceiving victims in romance scams. By allegedly altering their appearance with face-swapping software, they build trust under false identities to defraud targets of substantial sums.
Editor Notes: The Yahoo Boys are reportedly not so much a centralized group as a loosely connected network of individuals and small clusters engaging in cybercrime schemes. In French-language reporting, they are usually referred to as "brouteurs," while in English-language reporting, they are often identified as "Yahoo Boys." While the Yahoo Boys have reportedly been experimenting with deepfake technology since sometime in 2021 or 2022, the incident date of 05/01/2022 is an approximation in reference to their reported use of real-time deepfake technology for romance scams. See Incident 901 for a specific example of how three Yahoo Boys allegedly defrauded a French woman of $850,000 by posing as Brad Pitt. See Incident 912 for information on how they have reportedly been targeting widows. See Incident 913 for information pertaining to their use of AI to allegedly impersonate news organizations to blackmail victims.
Entidades
Ver todas las entidadesAlleged: Unknown deepfake technology developers developed an AI system deployed by Yahoo Boys , Scammers in Nigeria y Brouteurs, which harmed Targets of the Yahoo Boys , Targets of scammers in Nigeria , Targets of romance scams y Targets of brouteurs.
Sistema de IA presuntamente implicado: Unknown deepfake apps
Estadísticas de incidentes
ID
911
Cantidad de informes
1
Fecha del Incidente
2022-05-01
Editores
Informes del Incidente
Cronología de Informes
Los cumplidos comienzan a fluir tan pronto como ella responde a la videollamada. "Wow, eres tan bonita, cariño", dice el hombre al otro lado de la pantalla. Su transmisión de video muestra que es blanco, con cabello corto, probablemente uno…
Variantes
Una "Variante" es un incidente que comparte los mismos factores causales, produce daños similares e involucra los mismos sistemas inteligentes que un incidente de IA conocido. En lugar de indexar las variantes como incidentes completamente separados, enumeramos las variaciones de los incidentes bajo el primer incidente similar enviado a la base de datos. A diferencia de otros tipos de envío a la base de datos de incidentes, no se requiere que las variantes tengan informes como evidencia externa a la base de datos de incidentes. Obtenga más información del trabajo de investigación.