Fraud Using Deepfake: How This Technology Defrauded a Multinational Company of $25 Million

Fraud Using Deepfake: How This Technology Defrauded a Multinational Company of $25 Million

A finance worker at a multinational company was tricked into paying $25 million to fraudsters using deepfake technology. The fraudster posed as the company’s chief financial officer in a video conference call, according to Hong Kong police.

Chronology of Fraud Using Deepfake Technology:

The elaborate scam involved video calls with what the worker thought were several other staff members, all of which turned out to be fake recreations, Hong Kong police said in a briefing on Friday. “In a video conference attended by many people, it turned out that everyone he saw was fake,” senior supervisor Baron Chan Shun-ching told the city’s public broadcaster RTHK.

Chan said the worker became suspicious after receiving a message from the company’s UK-based chief financial officer. The message was suspicious because it contained a request to carry out a confidential transaction, but the worker’s doubts were dispelled after the video call. Others present looked and sounded like coworkers he knew, Chan said.

Impact of Deepfakes on Corporate Financial Security:

Believing that everyone who called was real, the worker sent a total of $200 million Hong Kong dollars – about $25.6 million. The case is one of several recent episodes in which fraudsters are believed to be using deepfake technology to modify videos and other recordings to scam people out of money.

Protective Measures from Deepfake Fraud:

At a press conference Friday, Hong Kong police said they had made six arrests in connection with the scam. Chan said the eight stolen Hong Kong ID cards were used to make 90 loan applications and 54 bank account registrations between July and September last year. On at least 20 occasions, deepfake AI was used to fool facial recognition programs by impersonating people depicted on ID cards.

Conclusion and Recommendations:

Recent studies on deepfake detection in photos have also shown promising results. One innovative method is the use of Convolutional Neural Networks (CNN) to analyze micro features on the face that are invisible to the human eye, such as skin texture, lighting patterns, and other small inconsistencies. For example, deepfake images often have subtle artifacts around the edges of the face or unnatural lighting changes on certain facial features.

Don’t worry because now deepfakes can be detected using machine learning. These detection technologies continue to develop and become more sophisticated, helping to identify and prevent the use of deepfakes for fraudulent purposes. Banking and financial institutions should invest in these detection technologies and train their staff to recognize potential signs of deepfakes. In doing so, they can minimize risks and protect the integrity of their financial systems.

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Published date :

12 June 2024