In the current digital world deepfakes have become a powerful and dangerous tool. AI generated images, videos and audio clips that are so realistic they can pass for real people. With deepfakes getting increasingly sophisticated, liveness detection has become the centerpiece in the arsenal of digital impersonation and fraud defense tools.

Liveness detection is a key layer of protection against deepfake attacks, and it works by verifying that a real, live person is present during a digital interaction. Without this technology, organizations all across the United States would continue to struggle to secure sensitive systems and authenticate the identities of their users.

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Understanding Deepfakes: A Growing Threat

Synthetic media that use artificial intelligence (AI) and machine learning techniques to create deepfakes. They can replace faces in videos, create fake voices, or make up an event that never happened.

Sensity AI said that the number of deepfake videos online has increased by 900% over the past three years, with the U.S. one of the most affected regions, according to a report published on Jan. 20, 2024. In politics, finance, entertainment and even personal relationships, these manipulated media forms take risks.

Detection tools for deepfake are important, but as the attackers become more skilled, detection alone is not sufficient. Liveness detection technology comes in to help fortify the defense.

What Is Liveness Detection?

This is what liveness detection is, when technology is used to determine whether a person interacting with a system is physically present and alive. Face liveness detection, unlike traditional forms of verification such as passwords or PINs, leverages biometric cues such as eye movement, blinking, skin texture, and even blood flow to confirm a real person.

Two main types of liveness detection software are:

Active liveness detection – requires the user to perform some action, such as blinking or turning their head.

Passive liveness detection – It works in the background, passively analyzing data without the need for any user action.

Both these methods are proving to be crucial in the defense against AI deepfakes and in stopping identity fraud.

How Liveness Detection Combats Deepfakes

This is the blocking of fake identities at the door.

The sectors where deepfakes could be most harmful — banking, healthcare, and online voting — are also where they are frequently used to get around identity verification processes. Organizations can implement liveness detection technology to reject deepfakes (video or image) before they are allowed in.

In online banking, for instance, liveness detection software prevents people from using pre recorded or AI generated videos to apply for a loan or access an account, by ensuring the person applying or accessing the account is physically present.

Enhancing Deepfake Detection Systems

Even though deepfake detection software can detect manipulated media, it may not be able to keep up with constantly evolving techniques. However, when combined with liveness detection, systems get an extra level of verification. A deepfake may be flawless to the human eye or fool detection software, but it will probably fail face liveness detection.

Real-World Applications in the USA

Financial Services

Liveness detection software has been adopted quickly by US banks and fintech companies looking to battle fraud. The American Bankers Association says that financial fraud losses reached $8.8 billion last year, much of it attributable to digital scams, including deepfakes. Liveness detection technology has become essential to add to identity verification processes to help reduce these losses.

Healthcare

Telemedicine has become more popular in the U.S. — particularly after the COVID-19 pandemic. Liveness detection provides assurance that the patient on the other side of the screen is who they say they are, preventing insurance fraud and protecting patient data.

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Government and Public Services

U.S. government agencies, from online tax filings to remote voting pilots, are looking into liveness detection to verify the identity of citizens in digital interactions and protect them from deepfake driven fraud.

Challenges and the Path Forward

However, liveness detection technology is not without its challenges. It should be robust enough to deal with edge cases like different skin tones, different lighting conditions, and accessibility requirements. Furthermore, with deepfake technology becoming more advanced, attackers may try to spoof liveness signals and thus force detection systems to keep up to date.

In response, deepfake detection and liveness detection software is being poured into by tech companies and researchers alike. Microsoft and Meta and other major players are partnering with universities to create advanced solutions that will stay ahead of the bad actors.

Statistics and Trends to Watch

Juniper Research forecasts that by 2026, more than 90 percent of U.S. smartphone users will have access to some form of biometric authentication and liveness detection will be key to that.

Liveness detection is an important part of digital security, and the market for it is expected to grow by 23% annually through 2028.

According to data from The Financial Brand, nearly 80 percent of U.S. banks are piloting or already using liveness detection to combat deepfake and identity fraud, set to be more than 75 percent by 2024.

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Conclusion

With AI deepfakes, verifying identities has become harder than ever — or more critical. The liveness detection provides a crucial defense for systems, making sure that they are not interacting with synthetic imposters, but with real people.

Liveness detection in conjunction with deepfake detection can help organizations across the U.S. enhance their defenses, protect sensitive information, and uphold trust in digital transactions. As deepfakes become more advanced, liveness detection will become ever more important in the battle against these threats.