How identity fraud by deepfakes is challenging identity verification?

In recent years, Artificial Intelligence has seen remarkable advancements, particularly in the realm of deep learning. One of the most alarming applications of this technology is the creation of deepfakes – the highly realistic but fake videos and audio recordings. While deepfakes have been used for entertainment and artistic purposes, their potential for misuse is significant, especially in the context of identity verification and fraud.

Let’s see how identity fraud by deepfake works and the different ways to fight it.

Identity Fraud by Deepfake: when AI becomes a threat

Deepfakes leverage AI and machine learning algorithms to create convincing fake images, videos, and audio of real people. These fakes can be used to impersonate individuals in various scenarios, from accessing secured systems to defrauding companies and individuals. The process typically involves:

1. Data Collection: Gathering images, videos, and audio of the target individual. This data is often sourced from social media profiles, public appearances, and other online platforms.
2. Model Training: Using this data to train AI models that can mimic the target's facial expressions, voice, and mannerisms.
3. Synthesis: Generating the deepfake content, which can then be used to deceive human operators or automated systems.

The proliferation of deepfake technology has led to several high-profile cases of identity fraud and misuse:

• Celebrity Impersonations: Deepfakes of celebrities have been used to create fake endorsements or manipulate stock prices by falsely portraying them making significant business announcements.
• Financial Scams: In early 2024, a UK-based company was tricked into transferring $25 million to fraudsters after a finance employee was convinced he was speaking to his boss, whose voice was mimicked using deepfake technology.
• Political Misinformation: Deepfakes have also been used to create fake videos of politicians, potentially influencing elections and public opinion.

The lack of concrete data on deepfake-related identity theft makes quantifying the problem difficult. However, a 2020 report by PIXM estimated that synthetic identity fraud could cost businesses a staggering $1 trillion globally by 2022. These figures highlight the financial burden deepfakes pose for companies and individuals alike.

Identity Fraud by Deepfake: Fighting AI with AI

Identifying deepfakes remains a significant challenge. A study by the Idiap Research Institute revealed that only 24% of participants could correctly identify deepfakes, even when expecting them. This percentage drops further in real-world scenarios where individuals are not primed to detect such frauds. Video interviews, a common practice in remote job applications, are particularly vulnerable as deepfakes can simulate bandwidth issues or other video artifacts to disguise imperfections.

Given the growing threat of deepfakes, there is an urgent need for effective countermeasures. The promising solution is the use of facial biometrics for liveness detection to ensure that the biometric data being analyzed comes from a live person rather than a pre-recorded video or fake video. But the rise of AI-generated content poses a significant threat to the integrity of biometric security systems. According to Gartner, Inc., 30% of companies may question the reliability of facial biometric technologies by 2026, emphasizing the urgent need to address this growing issue.

To safeguard against identity fraud by deepfake, organizations must partner with liveness detection providers, such as Unissey, who proactively address deepfake vulnerabilities and adapt swiftly to new challenges. Staying ahead of these threats requires the implementation of the latest security measures and constant vigilance in monitoring the exploitation of deepfake technology.

Because, not all liveness detection solutions are equal. Fighting presentation attacks is a basic need, and even in that, for some of them, their level of security might not attain the requirements of the ISO30107. But that’s another story… When we know that most of identity frauds by deepfake are made through video injection attacks, which increased by 200% in 2023 according to the same Gartner Research, it becomes obvious that businesses must equip themselves with liveness detection solutions that not only cover all types of attacks but have also proven their robustness against highly sophisticated attacks. Find out how to choose your facial biometrics partner in this article.

Conclusion

As deepfake technology continues to evolve, so must the strategies to detect and prevent its misuse. Modern organizations need to invest in robust, certified liveness detection solutions to safeguard their systems against these sophisticated threats. By leveraging advanced biometric technology and staying ahead of fraudulent innovations, businesses can protect their sensitive information and maintain the integrity of their operations in an increasingly digital world.