Crypto losses to deep fakes could reach $25B in 2024 — Bitget

Felix Ng59 minutes agoCrypto losses to deep fakes could reach $25B in 2024 — BitgetCriminals are using deep fakes more than ever, causing $79.1 billion in losses since 2022.291 Total views2 Total sharesListen to article 0:00NewsOwn this piece of crypto historyCollect this article as NFTJoin us on social networksCrypto losses to deep fake tricks and scams are set to reach over $25 billion in 2024 — more than doubling last year’s losses, according to Bitget Research.

The crypto exchange noted a 245% increase in the number of deep fakes worldwide in 2024 in a June 27 report, drawing from earlier Sumsub research data.

Bitget found that China, Germany, Ukraine, the United States, Vietnam and the United Kingdom had the most deep fakes detected in the first quarter of 2024, while the crypto industry saw a 217% gain compared to Q1 2023.Forecast for losses from crypto-related deep fakes with forecasted value for 2024 Source: Bitget Research

Bitget said the rise in deep fakes led to $6.3 billion in crypto losses in the first quarter. It added that it saw losses rising to $10 billion per quarter by 2025. 

“Deepfakes are moving into the crypto sector in force, and there is little we can do to stop them without proper education and awareness,” Bitget CEO Gracy Chen told Cointelegraph in a statement.Crypto losses to deep fakes, broken down by type. Source: Bitget Research

Interestingly, deep fake fraudsters haven’t changed their tactics too much over the years. 

Most crypto losses to deep fakes occur in the form of fake projects, phishing attacks and Ponzi schemes, where deep fake technology is used to gain the trust of cryptocurrency investors.

Over the last two years, this method has made up for more than half of all deep fake-related crypto losses.

“By impersonating influential figures, these schemes create the illusion of credibility and substantial project capitalization, thereby receiving large investments from victims without thorough due diligence,” said Bitget Research.

MicroStrategy executive chairman Michael Saylor has been a popular choice for fraudsters. In January, Saylor said his team removes around 80 artificial intelligence (AI)-generated fake videos of him daily, usually to promote some kind of Bitcoin (BTC)-related scam.Source:Michael Saylor

Bitget noted that deep fakes are used in other ways, including cyber extortion, identity and impersonation fraud and market manipulation. For example, a fake statement from an influencer or news anchor in an attempt to manipulate the price of a token. However, these made up a much smaller share than crypto scams. Deep fakes could be used in 70% of crypto crimes

Without effective measures in place, the share of deep fakes being used in crypto crimes could reach 70% by 2026, Bitget predicts.

“Criminals are increasingly employing fake photos, videos, and audio to exert a stronger influence over their victims,” Bitget Research chief analyst Ryan Lee told Cointelegraph.“For instance, a video impersonating someone close to the victim could be pivotal for fraudsters, whereas a fake video of an influencer might bolster investor confidence in a scam project as an ancillary tool.”

Lee believes one of the more immediate concerns over deep fake technology is the use of AI-backed voice impersonators, which allow scammers to call users pretending to be their relatives and ask for money.

Related:Elaborate Elon Musk deepfake crypto fraud uncovered in Hong Kong

Another could be deepfakes that circumvent Know Your Customer (KYC) measures to gain unauthorized access to a user’s funds.

“Right now, exchanges need to pay attention to their ‘Proof of Life’ features of the KYC systems the most,” said Lee.“This feature essentially confirms that the user is a real person and not a static image or a video, through real-time actions like blinking, moving or secondary ‘Proof of Life’ requests.”

“We warn all our users upon registration that we use advanced AI solutions to quickly identify and prevent cases of deepfake usage,” he added.

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