Jun 02, 2026 10 min read How Fraudsters Bypass Facial Recognition and Stay Hidden in 2026 Discover how fraudsters attack facial recognition with deepfakes and spoofing, and how liveness detection helps stop biometric attacks. Luke Owain Boult Content Writer Since the dawn of facial biometric verification, fraudsters have been looking for ways to bypass facial verification—from simple paper masks to sophisticated deepfake technology. Sumsub's Q1 2025 fraud trends research found that synthetic identity document fraud in North America rose by 311% year over year, while deepfake fraud surged by 1,100%. The sharp increase highlights how fraudsters are increasingly using AI-generated documents, faces, and biometric data to evade identity verification systems. Synthetic media is rapidly becoming a weapon of choice for fraudsters targeting financial institutions. The misuse of deepfake technology extends far beyond financial scams. In 2025, the FBI warned that North Korean IT workers were using false identities to obtain work at US companies to raise funds for the sanctioned regime. There is growing evidence that these fraudsters are using real-time deepfake technology in remote job interviews, enabling impostors to animate stolen, purchased, or synthetic identities with AI-generated video and audio during virtual interviews and onboarding. A deep dive into how dangerous deepfakes are and how they are created as well as how much they cost to produce This article explores how fraudsters bypass biometric face recognition systems and what companies can do to verify that the person on screen is real, present, and legitimately linked to the identity being verified. How facial recognition works Biometric facial recognition is a technology that identifies or verifies individuals by analyzing their facial features. It works by mapping key biometric markers in a selfie or short video, such as the distance between the eyes or face shape, and comparing them with a stored facial template. Facial recognition vs facial verification There is, however, a distinction between facial recognition and facial verification. Facial recognition typically scans faces in public or private databases to identify individuals. In contrast, facial verification confirms a person’s identity by matching a real-time image against a specific record, such as the face on an ID document. Is biometric face recognition safe? Face biometrics help protect both businesses and the general public from deception, but without robust anti-fraud mechanisms like liveness detection, some systems can be fooled by spoofed images, pre-recorded videos, or AI-generated synthetic faces. This is why strong systems do not rely on face matching alone. They also check whether the person is physically present, whether the image or video has been manipulated, whether the device looks suspicious, and whether the identity data matches other risk signals. The goal is not only to check that two faces look alike, but to confirm that the person is real, present, and not using manipulated media or a compromised device. This, as well as privacy obligations, makes it critical for businesses to understand how their biometric facial recognition systems work and what their vulnerabilities are. Where face recognition is used today Facial verification is widely used in onboarding, account access, AML/KYC checks , and fraud prevention across industries such as banking, fintech, crypto, and iGaming. As adoption has grown, so has the incentive for criminals to find ways around these controls. Rather than attacking the underlying biometric algorithms, many fraudsters focus on deceiving the verification process itself—using fake faces, manipulated media, or compromised devices to appear legitimate. Broadly speaking, these attacks fall into two categories: Presentation attacks , which attempt to fool the camera with fraudulent visual inputs System-level attacks , which target the way biometric data is captured or transmitted Method 1: Presentation attacks (spoofing facial biometrics) Presentation attacks, also known as face spoofing, happen when fraudsters present fraudulent facial evidence to bypass biometric authentication. Instead of attacking the back-end infrastructure, they try to fool the verification technology with something that looks like a real person’s face. This is where anti-spoofing measures become essential. Strong biometric systems need to detect not only whether a face matches an identity document or account record, but also whether the face belongs to a real person and has not been copied, artificially generated, or physically disguised. Here’s how fraudsters try to cheat facial recognition systems. Using stolen pictures and photos In the era of social media, fraudsters can obtain almost anyone’s picture and use it to try to fool facial verification. If facial biometric technology does not analyze certain characteristics of an image like depth, movement, texture, or other signs ...