Essential Guide

Advanced Deepfake Detection

This deepfake detection guide covers what deepfake fraud is, why it matters to security leaders and employees, and outlines strategies for organizations to protect themselves against sophisticated identity fraud tactics that can diminish trust and profits.

What’s Covered

Learn about identity-related threats facing businesses and consumers as deepfakes, generative AI and video injection attacks become more accessible and harder to detect.

Chapter 1

The Continued Rise of AI and Deepfakes

What is a deepfake?

Deepfakes are highly realistic, digitally manipulated images, videos or audio recordings where a person’s likeness, voice or actions are artificially created or altered using deep learning techniques. These tools can generate convincing media that appears real but is, in fact, synthetic.

Deepfakes are more than fake videos. Fraudsters can use AI-generated faces, face swaps, synthetic selfies, cloned voices and manipulated IDs to impersonate real users, create synthetic identities or bypass remote verification checks. Deepfakes often use techniques like generative adversarial networks (GANs) to blend and map one person’s facial expressions or voice onto another.

GIF of faces interchanging showing a deepfake swap.

The Growing Surge of Deepfakes

95%

of consumers know what a deepfake is, but only 54% think they could spot a deepfake video. (Jumio)

74%

of consumers say that deepfakes are an ongoing concern for them. (Jumio)

62%

of organizations experience a deepfake attack each year. (Gartner)

30%

of organizations experience a deepfake video attack using against automated face biometrics or identity verification each year. (Gartner)

11%

Synthetic fraud represented 11% of all reported fraud by Q4 2025, up from just 1.4% the previous year. (LexisNexis)

$40B

Generative AI-driven fraud will reach approximately $40 billion in 2027, up more than threefold since 2023. (Deloitte)

In 2026, fraudsters created and distributed two deepfake videos of the CEOs of India’s Bombay Stock Exchange and National Stock Exchange, each claiming to offer tips of hot stocks to buy. Each CEO and stock exchange had to issue statements clarifying their impartiality and warning investors against purchasing stocks based on the videos’ claims.
BBC NEWS

Chapter 2

Deepfake Tactics and Tools

Deepfake videos are created in two ways. Fraudsters can use an original video source of the target (where the video is manipulated so the person looks like they are saying or doing things), or they can swap one person’s face onto a video of another individual, also known as a face swap.

image of two women photos. Woman on the left has short to mid brown hair. Woman on the right has same hair but different face.

Common Deepfake Tactics

Modern deepfake fraud goes further than video manipulation. Attackers combine synthetic faces, fake documents, cloned voices, virtual cameras and injected media streams to target identity verification workflows.

black and white image of two faces being morphed.

Face morphs

A face morph merges two or more faces into one image that retains biometric characteristics from multiple people. Fraudsters use morphs to create documents or selfies that appear to match more than one person.

Synthetic faces

Face synthesis tools create fake faces using generative AI. When paired with synthetic or manipulated identity data, these images can support fake account creation and synthetic identity fraud.

black and white image of split screening showing one half of face and another.

Face manipulations

Fraudsters can alter attributes such as hair, eye color, skin texture or facial shape in photos and videos. These manipulations are often used alongside face swapping.

Synthetic identity documents

AI tools can help create convincing fake driver’s licenses, passports or supporting identity evidence. Fraudsters may also modify the face image on an otherwise realistic document.

Video injection

Video injection bypasses the physical camera by introducing fake, pre-recorded or manipulated media directly into the data stream. This could even be images that are of real people, but harvested from social media or other sources. Video injection can make an identity verification platform believe it’s receiving a live camera feed when the content is synthetic, pre-recorded or manipulated.

Tools Used to Create Deepfakes

High-end, custom-built solutions

More capable fraudsters combine separate tools to generate or alter a face, clone a voice, synchronize lip movement, and refine the final video. This “toolchain” approach lowers the barrier to creating deepfakes because the fraudster doesn’t need to build the underlying AI models.

The most sophisticated fraudsters use open-source deepfake frameworks, custom-trained models, virtual cameras, synthetic identity assets, and digital injection techniques, which enable them to feed synthetic media directly into an onboarding or authentication session.

While these two approaches continue to be used, the speed at which off-the-shelf, AI-based solutions have increased their sophistication is rapidly diminishing their benefits. As a result, custom-built solutions are far less prevalent today than just 2-3 years ago, and their use will continue to decline.

Dedicated infrastructure

Organized fraud operations maintain their own infrastructure, including libraries of stolen or synthetic identity data, automated scripts, device farms, custom model training, and quality-control processes designed to identify which variants are most likely to bypass verification checks. Fraudsters using higher end hardware can also perform face swaps in real time, allowing them to react to active liveness prompts in real time, even when doing a face swap.

As deepfake creation becomes cheaper and easier, the threat is shifting from isolated fake videos to industrialized identity deception, where AI-generated faces, voices, documents, and behavioral signals are combined into coordinated fraud campaigns.

Low-end consumer tools

Web-based face-swap tools, avatar generators and video filters that require little technical skill can produce synthetic profile videos, altered selfies, or short clips that appear realistic enough for low-friction onboarding flows, social engineering, or account takeover attempts.

Chapter 3

How Video Injection Works

What is video injection?

Video injection is a digital injection attack where fake data, such as AI-generated documents, photos, biometric images or pre-recorded video, is introduced into the data stream of an identity verification platform.

This technique bypasses the use of a physical camera by using emulators, virtual cameras or other software to simulate a physical camera. It can bypass the platform’s ability to verify that a real user is physically present in front of the camera.

How do injection attacks impact identity verification?

Injection attacks are challenging for identity verification systems that rely on video-based authentication for a number of reasons:

The attacks bypass facial recognition, basic liveness and ID checks by mimicking a legitimate user.
The attacks trick less sophisticated systems that fail to recognize the difference between genuine live input and injected video.
Attackers impersonate legitimate users to open accounts, pass onboarding checks or authorize financial transactions.
Successful attacks erode trust in the system, leading to customer dissatisfaction, fraud losses and potential regulatory exposure.

Why are injection attacks becoming harder to stop?

Fraudsters don’t need to rely on printed photos, masks or pre-recorded videos shown to a camera, as video injection attacks the capture process itself. This makes it more important for businesses to evaluate not only the biometric image, but also how that image was captured and whether the stream is authentic.

Chapter 4

Liveness Detection

What is liveness detection?

Liveness detection is used by identity verification solution providers to ensure that the biometric sample being presented is from a live person rather than a static image, video, mask, manipulated selfie or injected stream.

It’s a crucial feature in online verification and authentication solutions, as it can prevent spoofing attacks where someone tries to create an account or gain unauthorized access by presenting a fake representation of another person’s biometric trait.

Detection Strategies

Active liveness detection

The user is prompted to perform a specific action, such as blinking, nodding, smiling or turning their head. This method makes it harder to replicate a live person with a static image or mask, but it can introduce unnecessary friction and lower overall conversions.

Passive and semi-passive liveness detection

Passive detection analyzes subtle characteristics of live biometrics without asking the user to perform a specific action. This is often more seamless for the user, but it requires sophisticated algorithms and strong fraud detection capabilities. Semi-passive detection may not ask for a specific movement from the user, but uses visual cues, camera behavior or other signals to detect depth, motion and physical presence.

Motion analysis

Motion analysis assesses the subject’s movements to determine if it’s a live person, looking for natural motion and behavior patterns that a real human would produce.

Balancing Strong Liveness and a Smooth UX

Liveness detection is the core of remote identity verification, and while all solutions have now incorporated it, the real differentiator is how effectively these solutions can detect advanced deepfakes, while maintaining a seamless user experience.

Overly burdensome liveness checks can frustrate users and lead to onboarding abandonment. On the other hand, checks that are too lenient or simplistic may fail to detect sophisticated spoofing attempts, compromising security.

Striking the right balance combines advanced AI and biometric technology to deliver robust fraud detection without adding unnecessary friction. The ideal liveness detection system is fast, accurate and intuitive, providing clear instructions while identifying spoofing attempts, such as video injection attacks, replay attacks and AI-generated images in the background.

Ultimately, user trust depends on a solution’s ability to provide both security and a smooth experience, ensuring customers feel protected without being inconvenienced.

Chapter 5

Liveness Detection Use Cases

Liveness detection can be used to protect consumers and businesses across a wide range of industries. Some examples of these include:

Financial Services

Coerced account opening prevention
Account takeover prevention
Unauthorized high-risk transactions
Mule account prevention
Synthetic identity fraud prevention

Gaming

Fake or duplicate account opening prevention
Age restrictions
Bonus abuse prevention
Account takeover prevention

Shared/Gig Economy

Passenger and guest protection
Driver and host protection
Account takeover prevention
Fake profile prevention

Marketplaces and Digital Platforms

Seller and buyer verification
Fraudulent account creation prevention
High-risk transaction checks
Trust and safety support

Chapter 6

Security Standards & Liveness Detection

Deepfake detection standards are still in their early stages. The current standard – published in early 2025 – is CEN TS 18099:2025, which focuses on the threat delivery mechanism (i.e., camera injections).

A future development is ISO/IEC AWI 26655, Information Technology – Biometric Deepfake Attack Detection – Testing and Reporting, which is currently under development by ISO/IEC JTC 1/SC 37. This standard will focus on the payload (i.e. the image) and not on the delivery mechanism. However, publication is not expected until approximately 2029.

In contrast, biometric security already benefits from mature standards for presentation attack detection (PAD), such as the ISO/IEC 30107 series. These focus on defending against spoofing attempts using photos, video replays, masks, and other physical attacks presented to a camera or sensor. Deepfakes introduce a more sophisticated threat, particularly when synthetic media is injected directly into digital systems rather than physically presented to a sensor, creating challenges that existing PAD standards were not originally designed to address.

Chapter 7

Choosing the Best Capture Channel

It’s crucial to choose the right capture channel for liveness detection, to minimize risk while maximizing convenience for users. Different channels offer varying capabilities to ensure accurate verification while maintaining a user-friendly experience.

Mobile phones and apps

Mobile phones and apps are particularly well-suited for liveness detection thanks to their combination of advanced hardware and high-quality cameras, consistent configurations, and seamless integration with biometric features.

Laptops/PCs

Laptops/PCs with browser-based verification may present challenges such as inconsistent camera quality, browser variability and vulnerabilities to fraud, and they are far less widely used, especially in developing countries.

Expand Your Knowledge

7 Questions to Ask Your Liveness Detection Vendor

Watch the Webinar Replay

Chapter 8

How Jumio Can Help

Watch a Quick Explanation of Jumio Liveness

Providing the reliability, adaptability and security businesses trust to stay protected.

Next-Gen Liveness Detection

AI-driven technology

analyzing real-time user behavior to prevent deepfakes, masks and spoofing attempts.

ISO/IEC 30107-3 compliant

meeting rigorous industry standards for biometric security and fraud prevention.

Conforms to NIST/NVLAP testing standards

ensuring top-tier accuracy and reliability.

Detects sophisticated fraud

while staying compliant, delivering a secure and seamless customer experience.

Covering Industry Standard Checks

Built on ISO compliance standards, ensuring that our checks meet rigorous industry requirements for security, accuracy and reliability.

ID image used as
selfie

Paper
printouts

Digital
copy

Face
masks

Beyond the Standard

Image Quality Checks

Incorporating advanced image quality checks to help ensure precise verification, detecting fraud with accuracy even in challenging environments.

Face not fully visible

Is there a covering over the face?

Multiple people

Is there more than one person in the image?

No face present

Is there a nose, mouth and eyes?

Black and white image

Is the image in color or black and white?

Deepfake Detection

Leveraging cutting-edge deepfake detection technology to identify even the most sophisticated synthetic fraud attempts, safeguarding the integrity of your identity verification process.

Synthetic images

Face / head swaps

Injection and Replay Detection

Including robust camera and video injection detection, blocking fraudulent attempts at bypassing verification by using pre-recorded or manipulated content.

Additional Fraud Checks

Offering advanced fraud prevention features, including detecting sleeping individuals and manipulated selfies. Identifying inactive users and tampered images to help ensure only genuine, alert individuals pass verification.

“Online platforms hold a critical duty to leverage cutting-edge detection measures like multimodal, biometric-based verification systems to fortify our defenses against deepfakes."

Daryl Huff, Vice President of Biometrics, Jumio

Learn more about the world of biometric security.

Biometrics & Fraud Analytics: Stopping Fraud at the Source