How FACIA Detects Fake Videos in Seconds & Prevents Deepfake Fraud

Imagine losing millions to a face that doesn’t even exist. What if the face on a screen could drain millions from a system, without ever being real? Deepfakes are no longer science fiction. They are rewriting the rules of digital trust.

A report released by the United Nations Office on Drugs and Crime (UNODC) in October 2024, “Transnational Organized Crime and the Convergence of Cyber-Enabled Fraud, Underground Banking and Technological Innovation in Southeast Asia: A Shifting Threat Landscape, Technical Policy Brief,” states some terrifying statistics. In the sole year of 2023, the cyber-enabled fraud has drained an enormous amount of $18 to $37 billion from East and Southeast Asia. Most of this monetary accumulation is tied to organized crime groups that have been exploiting the deepfake technology and financial institutions. 

Fraud and scam operations in the Association of South East Asian Nations (ASEAN) region have become a well-organized industry. These activities generate an estimated $27.4 billion to $36.5 billion each year. A major concern is the shocking 1,530% rise in deepfake crimes in the Asia-Pacific from 2022 to 2023. Additionally, there has been a 704% increase in face swap injection attacks in the second half of 2023.

Scammers around the world are using deepfakes to trick identity checks, but people are now spotting them faster than ever. FACIA uses advanced AI and biometrics to detect and prevent deepfakes in real time. This technology helps stop impersonation and prevents the sharing of manipulated digital media.

Isn’t that fascinating? 

We know it is, let’s discuss how exactly Facia catches deepfakes and prevents users from deepfake fraud. But before that, it’s important to understand just how serious the problem has become. The rapid rise of fake videos and deepfakes poses a growing threat that cannot be ignored. 

The Rise and Risks of Fake Videos in the Digital Age

Deepfakes are fake videos created using deep learning methods like Generative Adversarial Networks (GANs) and transformer models. These AI-generated videos feature virtual humans who display real facial expressions, voices, and movements, instead of relying on traditional video editing techniques.

In the past few years, deepfakes were not common and often created just for fun, like videos where people acted like Tom Cruise. Early deepfakes did not aim to mislead or cause harm; they were made for entertainment and as jokes.

More scammers are now using easy-to-use generative AI to create deepfakes for harmful purposes. They may try to steal someone’s identity or trick FinTech companies’ Know Your Customer systems. Deepfakes can also fake video connections in remote identification. In politics, fake videos spread misinformation to influence people’s opinions and damage the reputations of targeted individuals.

Deepfakes are dangerous in two main ways. First, they can be used to impersonate someone during remote hiring processes. Second, they contribute to the spread of false information by creating fake videos on social media.

Deepfake Fraud: Real-World Cases Expose a Growing Digital Crisis

Deepfake technology is now easier and cheaper to obtain. Organized crime groups are using AI tools to increase fraud, identity theft, and misuse of synthetic media on digital platforms worldwide.

Telegram: The Dark Marketplace for Deepfakes

People mainly buy and sell deepfake services on underground Telegram forums. Customers can get services like face-swapping, voice cloning, identity spoofing, and AI-driven chatbots from vendors at any time. These services are promoted for use in many different fields.

During February to July 2024, mentions of the term deepfake went up by more than 600% in the monitored Telegram discussions.

Deepfake Pornography and Identity Abuse

People are creating explicit images without consent using AI. Attackers make videos that trick victims, which are later used to blackmail and harm them psychologically.Sextortion campaigns now tend to rely on these types of tactics.

Deepfakes in Fintech and KYC Fraud

Illegal activities have discovered ways to make deepfake-enabled KYC fraud much smoother in the fintech industry. Now, deepfakes can trick facial recognition software which poses big challenges for banks and fintech organizations.

Since synthetic media is becoming more common, FACIA is able to detect it through its identity verification process. The solution was created for the main purpose of:

• Detect face swaps and deepfake videos used in identity spoofing attacks during digital onboarding.
• Authenticate real-time facial biometrics to ensure user presence and liveness.
• Prevent identity spoofing attempts that exploit AI-generated visual clones.

Sextortion and identity impersonation, which rely on deepfakes and imitated biometric verification, allow fraudsters to steal money and compromise bank security. In this position, FACIA carries out an extremely strong defense.

Deepfake Videos: Fueling Misinformation and Eroding Public Trust on Social Media

Deepfake videos are being used to help misinform the public on social media. With videos that look like political speeches or clips of celebrities, they manage to lie to and persuade the audience at a massive rate. This type of content is so easy to share that it generates confusion, destroys trustworthy brands and makes people question real sources. At first, videos may look harmless, but they can quickly result in serious misinformation that causes real problems.

FACIA Detects Fake Videos Before They Cause Harm

The technology consists of multiple types of AI models that accurately discover artificial videos. Meaning, off-site fakes can be countered within videos by detection methods, enabling their usage in stopping fake identity proofing for digital systems. It makes use of facial recognition, spotting tiny expressions and verifying if the person is alive to detect unusual movements and features in a face.

At the same time, the layers are designed to spot face swaps, use of deepfake materials and presentations that hide object details.

FACIA’s datasets are made up of diverse racial and ethnic groups and special steps are taken to prevent bias in the training process. Moreover, updating the training data often allows detection of new ways to trick systems. It makes social media platforms more able to resist new fake and synthetic threats.

FACIA Deepfake Detection VS Traditional Detection Methods

FACIA stands out compared to traditional methods due to its greater accuracy, speed, and ease of adjustment, especially in high-risk or large-scale use.

Sectors Where Facia’s AI Security Can Detect Fake Videos

FACIA’s AI video detection system helps diverse business sectors in preventing fraud by verifying user identities and detecting manipulated videos in real time. 

Detecting Fake Video During Remote Identity Verification

For instance, in banks, it uses facial recognition to catch attempts to evade authentication with different faces as well as replay efforts during the online loan application process.

However, in the case of remote onboarding, FACIA carries out facial recognition and checks to confirm that the user does not have a fake identity. 

Preventing CEO Fraud and Fake Video Scams During E-Meetings with FACIA

FACIA confirms claimant identities during online claims to protect against people impersonating others and from sophisticated deepfake attacks, where attackers pose as top executives or CEOs to approve false activities or disrupt regular operations in the company. In some cases, this applies to fake video fraud that is used to stop e-meetings.

Now, a question arises here: why is it a matter of seconds, and why does it need to be so quick with the detections? 

Why Real-Time Deepfake Detection Is Critical in Combating AI Fake Videos

Detecting deepfake videos in real-time is very important, as they could cause great damage if they are discovered after becoming known. This type of misleading information moves quickly through social media. With a live system, there is less risk of identity theft, financial troubles and damage to a company’s reputation.

Regulatory risk structures fit well with the ultra-fast model, providing an essential edge over slow models. Using R&D ongoing studies, it ensures its machine learning technologies keep up with new patterns in deepfakes. Solutions are updated with the assistance of cyber groups, policymakers and top leaders, who work together to introduce new methods for growing threats.

Take Control Before Deepfakes Do: FACIA Combats AI Fake Videos in Real Time

FACIA uses advanced techniques to detect fraud while placing users’ needs first. It offers a dependable solution for rapid, correct identity verification, helping to prevent many failed approvals—available in remote onboarding, airports, and secure transactions.

As it relies on advanced tech made for ensuring high-assurance identity verification, it is extremely good at catching deepfakes distorted by AI and preventing the spread of fake news through false videos on social networks.

FACIA’s technology helps prevent fraud by ensuring:

• Fast and frictionless spoof detection identity verification without compromising accuracy
• Real-time detection of deepfakes and synthetic media on multiple platforms to prevent misinformation.
• Smooth onboarding experiences while blocking identity spoofing attempts
• Compliance with evolving regulatory frameworks for secure authentication
• Continuous model updates to adapt the detection capabilities to new fraud tactics without disrupting users
• Advanced fake video detection to identify manipulated visual content used in scams, impersonation, or misinformation.

FACIA’s anti-deepfake detection technology not only preserves the integrity of remote identity proofing systems but also ensures accurate fake video detection to curb misinformation, safeguard reputations, and prevent downstream fraud.