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Incident 509: Scammers Deepfaked Videos of Victims' Loved Ones Asking for Funds over Facebook in Vietnam

Description: In Vietnam, to convince victims of their disguises when prompted, scammers deepfaked audios and videos of victims' friends and families asking them over Facebook to send over thousands of dollars.

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Entities

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Alleged: unknown developed an AI system deployed by scammers, which harmed Vietnamese Facebook users.

Incident Stats

Incident ID
509
Report Count
2
Incident Date
2023-03-23
Editors
Khoa Lam
Applied Taxonomies
CSETv1, MIT

CSETv1 Taxonomy Classifications

Taxonomy Details

Incident Number

The number of the incident in the AI Incident Database.
 

509

Special Interest Intangible Harm

An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.
 

maybe

Date of Incident Year

The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank. Enter in the format of YYYY
 

2023

Date of Incident Month

The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank. Enter in the format of MM
 

03

Date of Incident Day

The day on which the incident occurred. If a precise date is unavailable, leave blank. Enter in the format of DD
 

Estimated Date

“Yes” if the data was estimated. “No” otherwise.
 

No

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

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.
 
  1. 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

Incident OccurrenceBlooming techniques of face transplant, fake voice to borrow money with Deepfake applicationDeepfake posing grave danger as scamming tool
Blooming techniques of face transplant, fake voice to borrow money with Deepfake application

Blooming techniques of face transplant, fake voice to borrow money with Deepfake application

dttc.sggp.org.vn

Deepfake posing grave danger as scamming tool

Deepfake posing grave danger as scamming tool

vietnamnet.vn

Blooming techniques of face transplant, fake voice to borrow money with Deepfake application
dttc.sggp.org.vn · 2023
AI Translated

(DTTCO) - After the scam by text messages thanks to money transfer, now scammers quickly apply other more sophisticated tactics, through fake video calls thanks to Deepfake technology.

Fool users

After nearly a week of being cheated of near…

Deepfake posing grave danger as scamming tool
vietnamnet.vn · 2023

After being detected with tricks of sending messages to appropriate money, scammers are now using an even more sophisticated tool called Deepfake to make fake video calls to victims and ask for money transfer.

Nearly a week after discoverin…

Variants

A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.

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