- Deepfakes increasingly tempt users into fraud and scams
- AI-powered attacks now involved in 16% of breaches
- To spot counterfeits, watch for facial/audio issues and urgent requests for money.
Deepfakes, the technology that uses artificial intelligence (AI) to synthesize realistic audio, video and images of real people, has improved to the point that even attentive people can be fooled with relative ease.
By creating multimedia showing celebrities, politicians or tech leaders saying things they didn’t say or doing things they never did, internet fraudsters have successfully tricked people into authorizing fraudulent transfers or tricking innocent people into “investing” in fake projects on fake investment platforms. In some cases, they were even able to create a fake emergency (like a car accident or attempted hijacking), which prompted family members to rush to make payments.
Today, 16% of all breaches involve AI-based attacks (according to IBM data), with phishing and deepfakes among the most commonly cited methods, says Danny Mitchell, cybersecurity editor at Heimdal Security. TechRadar Pro.
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Four concrete steps to spot a deepfake
According to Mitchell, one way to spot a deepfake is to watch for unnatural facial movements or eye blinks.
“Deepfake videos can struggle with the subtle mechanisms of human expression,” Mitchell writes. “Watch for fuzzy facial edges, inconsistent blinks, or expressions that don’t match the emotion being conveyed.”
He also claims that the audio can sometimes be slightly off in deepfake videos. AI-generated voices may have a slight flatness or unusual rhythm, while background noise may sound artificial.
Additionally, deepfake videos often feature inconsistent lip movements because the synchronization between speech and lips is often imperfect. This is especially noticeable at faster conversation speeds.
Therefore, spotting deepfakes comes down to looking for pixels. But there are other ways to identify a fake, and that is to pay attention to the content of the message. Mitchell says urgent requests for money or sensitive information, which require users to act quickly, are the usual red flag users should pay attention to.
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