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How Liveness Detection is Combatting Digital Identity Fraud

How Liveness Detection Selfie Battles Against Digital Identity Fraud?

Author: admin | 20 Jun 2022

In today’s rapidly evolving digital landscape, the promise of a brighter future is shadowed by the ever-increasing threat of identity fraud. As the world embraces technological advancements, fraudsters have become more sophisticated, leading to significant financial losses and consequences for businesses.

To effectively combat identity theft, one of the most powerful solutions available is the implementation of Liveness Checks in the financial ecosystem. These checks verify the presence of the individual in real time, specifically through facial biometric verification. This method ensures that the person being verified is physically present, eliminating the use of masks or manipulated photos.

Consequently, many enterprises are transitioning from traditional ID checks to more reliable 3D video selfie checks, streamlining the onboarding process and fortifying security.

However, it’s vital to recognize that not all liveness solutions offer the same level of security, and fraudsters continuously develop advanced methods to circumvent these measures.

Major Frauds In the Digital Identity World

Remember the excitement when Apple Inc. introduced the iPhone X, causing a global sensation? This iconic moment also marked a significant leap forward in delivering top-tier liveness detection services worldwide.

For decades, biometric verification has been a cornerstone in validating individuals within the intricate landscapes of finance and the digital realm, cementing its status as one of the most rapidly advancing industries. Before we delve deeper into this subject, let’s embark on a journey to understand the multifaceted world of identity fraud attacks, all while seamlessly integrating liveness detection selfie.

The 5 Types of Major Frauds In the Digital Identity World

Spoof Attack

Wearing a mask is an old trick for fooling the biometric verification process. Fraudsters use silicone masks, pictures of other individuals, or even mannequins to hack into accounts. These frauds are known as “face spoofing.”

In the digital world, fraudsters can acquire pictures of almost anyone and use them for their benefit, such as by hacking into a system. This is why, when a verification technology fails to analyze the depth of the image, criminals can utilize social media-acquired images to hack into devices.

Another spoof attack is by fooling the verification system with pre-recorded videos. These pre-recorded videos can easily bypass the system, even when verification systems ask individuals to wink or blink.

Fraudsters also use masks to spoof the liveness detection, which is performed using a range of props such as paper masks or real-like mannequins. Additionally, silicone masks deceive verification technologies that do not perform skin texture analysis and other real-face characteristics.

These masks are so hyper-realistic that it is almost impossible to detect whether the fraudster is wearing a mask or is a real one. Criminals in France used hyper-realistic masks to impersonate the French Defence Minister. They successfully stole $90 million, stating they needed to give money to terrorists to save kidnapped individuals.

Presentation Attack

During a life check, biometric verification compares the user’s physical features, such as the distance between their eyes or nose. The mapped features are compared to a biometric template.

A fraudster can exploit the limitation when using face biometrics for authentication to trick the system into believing it sees the authentic user. This points out a presentation attack, and swindlers nowadays have more effortless access to high-definition shots and videos that can be used to cheat facial recognition software.

Likewise, liveness detection utilizes a biometric technique to estimate and interpret physical characteristics and responses to determine if a biometric sample is collected from a living, present subject or a still object. Instead of performing matching functionality, the technology detects presentation attacks too.

Bypass Attack

The second attack is when criminals hack the cameras by injecting pre-recorded videos, hacking servers, or editing previously uploaded biometric data.

Printed 2D Masks

A surprisingly cunning way to deceive the liveness detection is to use 2D-printed masks with holes in the eyes and mouth. This attack is typically successful because many liveness detection systems require individuals to blink or smile while verifying their identities. Thus, fraudsters place their eyes and mouth in these spaces so that the verification system cannot perceive them and conclude that the person is real, not just a picture.

Social Engineering

Usually, fraudsters do not opt for technical knowledge to deceive the liveness system; instead, they focus on ways to cheat the system using photos. This deception is performed using information collected from victims after confirming their data over a phone call or email.

The fraudsters then claim that they belong to a company that wants to send them gift hampers, and upon arriving with the gift, they ask for facial recognition for security purposes. Criminals can instantly access the user’s account and release funds using the secured data acquired from significant leaks.

The Power of the Liveness Detection Selfie

Liveness detection selfie technology offers a robust solution to combat identity fraud and enhance security in the digital financial world. Here’s how it achieves that:

Identity Verification

Liveness checks ensure that the person being verified is physically present, eliminating the use of masks or manipulated photographs. By utilizing facial biometric verification, businesses can authenticate individuals more reliably and streamline onboarding.

Biometric Recognition

Biometric recognition technologies, such as facial recognition, fingerprint scanning, and voice recognition, play a pivotal role in identity verification. These cutting-edge technologies analyze unique physical characteristics, providing precise and secure authentication.

Fraud Prevention and Detection

Comprehensive fraud prevention and detection solutions leverage advanced algorithms and machine learning to identify suspicious activities effectively. By implementing such solutions, businesses can proactively mitigate potential fraud incidents and safeguard their assets and customers. 

How To Choose the Right Identity Verification Provider

Selecting a reliable and trustworthy identity verification provider is crucial for businesses seeking robust security measures. Reliability, scalability, compliance, and customer support should be considered to ensure a provider selection that aligns with specific business needs.

How Facia Can Mitigate Identity Fraud?

Biometric verification and liveness detection play a significant role in mitigating identity fraud. Facia offers a robust 3D liveness detection system with critical features that help businesses combat sophisticated identity fraud attacks. Here are some of its notable features:

Anti-Spoofing’s system detects suspicious elements in user-uploaded videos, such as photoshopped, pre-recorded, static, or tampered videos.

3D Perception Techniques

The technology employs 3D perception techniques to analyse the video’s measurements and nodal points, ensuring authenticity.

Micro-Expression Analysis’s liveness detection system also performs micro-expression analysis, detecting minor facial movements in the live video.

Verification of Presence

The system ensures that the individual being verified is physically present, preventing the use of photoshopped or manipulated videos.

Facia’s biometric verification technology incorporates innovative features for a reliable liveness detection system. As cyberattacks and identity theft pose significant challenges, businesses increasingly rely on facial recognition technology to authenticate identities in less than 1 second.

Facia provides a go-to solution that verifies identities quickly and effectively, leaving no room for fake identities to bypass the system.

Final Thoughts

In conclusion, liveness detection selfie is a game-changer for the digital financial world. By implementing identity verification mechanisms and biometric recognition technologies, businesses can enhance security, prevent fraud, and build customer trust.

Facia empowers organisations to mitigate the inherent risks associated with identity fraud effectively. By deploying these cutting-edge solutions, businesses can cultivate a fortified digital ecosystem that bolsters the protection of critical assets and ensures the well-being of their esteemed clients.

Frequently Asked Questions

What is liveness detection using selfies?

Liveness detection, utilizing real-time selfies, serves as a security measure to confirm the genuine presence of a user, bolstering security in financial transactions and activities. This technology helps prevent fraud by ensuring that the person in the selfie is physically present during authentication.

What is liveness detection in digital finance?

Liveness detection is a security measure that verifies the authenticity of a user by analyzing their facial movements in a selfie. It adds a vital layer of protection against identity theft and fraud.

How does liveness detection enhance security?

Liveness detection enhances security by ensuring the user is physically present during authentication. This prevents fraudsters from using static images or videos, making digital financial transactions much safer.

Can liveness detection be fooled with a static image?

No, it cannot. Liveness detection relies on dynamic facial cues such as blinking, head movement, and changes in expression. Static images or videos cannot replicate these, ensuring robust security.

How will liveness detection shape the future of digital finance?

Liveness detection will shape the future of digital finance by making it more secure and trustworthy. It will reduce fraud, lower risks, and encourage greater participation in digital financial services, benefiting both users and providers.

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