Fraudsters do not read vendor brochures. They try to find weaknesses in verification systems until they succeed then use that method on a scale. This shows that we need a way to compare tools. If you just look at features, let’s go through each attack a fraudster would try. The best liveness detection software should be able to defeat all of them from tricks to advanced ones. This guide will walk through each attack. By the end you will know which questions to ask to find a product before it is used.
The Cheapest Attack Comes
Every fraudster starts with the easiest option. They hold up a photo of the victim, printed or on another phone screen to the camera. If that works, they do not need to try anything complicated.
What Defeats Photos and Screen Replays?
Good tools can tell the difference between a face and a flat image. Real skin scatters light more than paper or a screen. Screens also leave behind patterns that people cannot see but trained models can. When talking to liveness detection software providers ask how their product tells a face from a replayed one. The people who provide answers are more trustworthy than those who only use words.
Making Physical Masks by using Flat Images
When photos do not work attackers use masks. In some cases, they wear silicone masks, printed masks with eye holes, or even cut-out photos over their face
Why Deep Check Only is Not Sufficient?
A mask can fool deep checks because it is three-dimensional. The best tools look for signs of living tissue. They check for color changes from blood flow, natural micro-movements around the eyes and the way real skin stretches. Ask each vendor if their product was tested against mask attacks. Certified testing like iBeta evaluation includes these scenarios.
Taking the Camera into the Physical World
The technical attackers skip the camera. They feed pre-recorded or generated video directly into the verification stream. The software never sees the environment. The face on screen can be perfect because no camera captured it.
What Makes This Attack Break Weak Tools?
The top liveness detection software verifies the capture channel, confirming the video came from the device camera in time. A product that only analyzes the image has no defense here. This defense is at the system level, not the image level. Always ask vendors how they detect injected streams and many cannot answer, which is a problem.
Deepfakes Injections with Real-Time Information
Deepfakes are getting better every month at the top stage of the escalation ladder. Now generated faces can produce natural facial expressions and blink naturally as a response to prompts. The tools that are available today allow you to conduct an attack that was previously conducted by someone with special skills.
Why Doesn’t the Old Model Have Today’s Fake?
It is difficult to detect deep fakes since they move constantly. If a technique is one that is not produced by newer generators, the model will not recognise it. In order to improve their detection models, the strongest providers retrain them constantly against artificial media. Always ask when the model was last updated and how often the model is retrained. If the answer is confident and specific, it is a living product. A generic one points to an old tool.
You can also read about Rising Need for Strong Digital Verification in a Connected World.
Achieving a Buying Decision Based on an Attack List
These four attack families give you an evaluation framework. Confirm the tool reads liveness signals that a photo cannot fake. The confirmation testing covered physical masks and confirmed that the capture channel gets verified against injection. Confirm deepfake models get retrained on a schedule. Then run a pilot with your users on real devices in bad lighting on slow connections. A tool that passes all these checks deserves a place on any shortlist of liveness detection software.
One gap remains a perfect deployment. Every attack above involves pretending to be someone else. Liveness detection ends that pretense. It says nothing about the genuine person who passes. A real live, verified customer can still be sanctioned or politically exposed. Regulated businesses must close that gap at the same onboarding moment by screening verified identities against global risk data. This is where AML Watcher enters the workflow. It pairs continuously refreshed sanctions, PEP and watchlist screening with the identity checks in place. For teams comparing the liveness detection software the lesson holds across every deployment. The strongest setups treat defense and risk screening as two halves of one decision.
