Understand Deepfake Identity Fraud

Deepfake technology has burst onto the scene with both fascinating potential and alarming implications. While it opens up unprecedented creative possibilities, it also poses a growing threat to the security of our identities. This article explores how deepfake identity fraud works and its impact on society.

What is Deepfake Identity Fraud?

The term "deepfake" combines "deep learning" and "fake," referring to multimedia content (videos, images, audio) manipulated using artificial intelligence to simulate hyper-realistic faces or voices.

While these technologies can be used for legitimate purposes, such as film dubbing or archive restoration, they have also become a tool of choice for cybercriminals. In identity fraud, deepfakes enable fraudsters to bypass biometric verification systems by creating entirely fake identities or impersonating real individuals.

According to a report by Entrust, 40% of biometric attacks involve deepfakes. In Europe, deepfake fraud increased by over 780% between 2022 and 2023, demonstrating its significant impact.

What is the impact of Deepfake Identity Fraud?

For businesses, data theft, reputational damage, and especially significant financial losses make deepfake fraud a very serious threat. Remember the Hong Kong-based multinational that suffered a $25 million loss when an employee was tricked by a fraudster impersonating a senior executive using AI...

Identity theft doesn’t only target top executives. In some cases, scammers pose as employees to redirect their salaries. A common technique involves contacting the HR department under the pretense of changing banking details.

Moreover, one might think deepfake fraud only affects large corporations, but that’s not the case. From multinational giants to small local businesses, no company is safe. A Paris-based communications agency, for example, suffered a €530,000 loss after falling victim to a CEO fraud attack.

Individuals also suffer serious consequences. While identity theft for banking fraud is widespread, deepfake technology has also enhanced more insidious financial scams.

One such example is the "love scam", a type of romantic fraud. Though not new, it has become significantly more sophisticated with deepfake technology. Cybercriminals exploit people’s emotional vulnerability to extract money by creating the illusion of a genuine romantic relationship. AI makes these deceptions nearly undetectable.

The consequences for victims include financial loss, emotional trauma, and social isolation.

How does Deepfake Identity Fraud Work?

Deepfake fraud is often highly orchestrated. When impersonating a real person, fraudsters follow a structured approach, beginning with data collection. To make the scam as convincing as possible, they gather information about their target, often by scouring social media to learn about their habits, interests, and communication style.

Once enough data is collected, fraudsters use AI tools to create deepfake content. The most common techniques include:

• Morphing – Gradually transforming one face into another, creating a smooth transition.
• Face Swap – Replacing a person’s face with another’s in a video or image while preserving original expressions and movements.
• Face Animation – Adding movement to a static image.
• Lip Synchronization – Altering lip movements in a video to match a new dialogue.
• Voice Synthesis – Cloning a person’s voice (a 3-second recording is often enough to create a realistic replica).

Creating deepfakes doesn’t require advanced AI knowledge—powerful and freely available tools make identity fraud accessible to virtually anyone.

Finally, cybercriminals use psychological manipulation techniques such as creating a sense of urgency, promising financial gain, or exploiting authority to pressure victims into complying.

Is Deepfake Fraud always digital?

Contrairement à ce que l'on pourrait penser, un deepfake n'est pas systématiquement associé à une attaque numérique. Bien que les deux concepts soient souvent confondus, ils présentent des différences significatives : 

• Un deepfake est un contenu généré ou modifié par l'IA, qui peut être utilisé de diverses manières, pas uniquement pour des attaques.
• Une attaque numérique (ou par injection), en revanche, consiste à introduire un fichier malveillant (qui peut être un deepfake, mais pas nécessairement) dans un système de capture pour contourner les mécanismes de sécurité.

Contrary to popular belief, deepfake fraud is not always digital. While the two concepts are often confused, they have distinct differences:

• A deepfake is AI-generated or modified content, which can be used in various ways—not just for fraud.
• A digital attack (or injection attack), on the other hand, involves injecting a malicious file (which may be a deepfake but not necessarily) into a capture system to bypass security mechanisms.

Check out this article to learn more about video injection attacks.

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Conclusion : 

Deepfake identity fraud is a growing and sophisticated threat to both businesses and individuals. Its ability to generate highly realistic content, combined with the easy accessibility of creation tools, makes it a major challenge for modern cybersecurity.

Fortunately, solutions exist, such as liveness detection, which ensures a real person is behind the screen. Unissey, a leading provider of facial biometrics identity verification, develops highly effective liveness detection technology to combat deepfake fraud.

Find out in this article why our solution is the ultimate safeguard against deepfake identity fraud.

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