> For the complete documentation index, see [llms.txt](https://spoofsense.gitbook.io/spoofsense-developer-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://spoofsense.gitbook.io/spoofsense-developer-documentation/introduction.md).

# Introduction

**Spoofs** or **Presentation Attacks** are representations of human face, used by Fraudsters to fool face biometric systems and carry out identity theft.

SpoofSense Facial Liveness distinguishes a live person from a spoofing attack. This process is called **Liveness Detection** or **Presentation Attack Detection (“PAD”)**. 

Unlike most other systems that perform PAD, SpoofSense Facial Liveness does not require any action or movement. It requires just a single-frame selfie image, the same image that is often used by the facial biometric system to carry out face match. SpoofSense Facial Liveness is a passive liveness solution.

Fraudsters use the following Presentation Attacks for Spoofing:

* **Printed Photo Attack** - A fraudster prints photos of an individual and presents it in front of the camera during verification.
* **Video Replay Attack** – A fraudster uses a digital screen of a phone or an iPad and presents a video of an individual being played on the digital screen in front of the camera to fool the verification system.
* **Printed Mask Attack** – A fraudster places a cut out photo in front of their face during verification, often with cut-out holes so the impostor can blink, a common test of liveness for other systems.
* **3D Mask Attack** – A fraudster creates a silicon or resin mask of an individual and presents it in front of his face during verification.

SpoofSense Facial Liveness requires just a single image of the user, taken from the front camera of their phone. 

**SpoofSense On-Premise Docker Image:**

* Facial Liveness Detection solution packaged as a self-hosted REST API docker container. 

SpoofSense returns a score and probability. You will use the probability to make a decision about liveness. The remainder of this documentation describes technical requirements and image samples and further provides instructions on how to run the SpoofSense Facial Liveness API.