Build Liveness Detection in Android – Prevent Face Spoofing

Build secure Android apps with real-time liveness & spoof detection using TensorFlow Lite — no paid APIs, fully offline

What you'll learn
- Build a real-time liveness detection (anti-spoofing) system inside an Android app
- Perform offline liveness detection without using any paid APIs or SDKs
- Integrate and run a TensorFlow Lite model for detecting real vs. fake faces
- Detect spoof attempts like photos, videos, or masks using a free custom-trained model
- Display live camera feed and recognize faces in real-time
- Add liveness detection to an existing face recognition Android app
- Understand how TensorFlow Lite models work in Android apps
- Load and use TFLite models efficiently for on-device ML inference
- Secure face-based apps like attendance, login, or verification systems

Requirements
- Some Basic knowledge of Android App Development with Java or Kotlin
- A computer with Android Studio or Visual Studio installed (Windows/macOS/Linux)
- No prior machine learning experience required – everything is explained

Description
In this hands-on course, you'll learn how to build areal-time liveness detection system(also calledspoof detection) directly inside an Android app —without using any paid APIs or cloud services.

Liveness detection helps confirm that the face in front of the camera is areal, live person— not just aphoto,video, ormask. It’s a powerful feature used inbanking apps,secure logins,KYC systems, and more.

What Makes This Course Special?

You’ll start by importing acomplete real-time face recognition Android app(code provided in the course).

New students: Everything is explained from scratch, including how the app works.

Returning students: If you’ve taken my face recognition course, you canskip directly to the liveness detection sectionto enhance your existing app.

Then, you'll learn to integrate acustom-trained TensorFlow Lite modelthat performsreal-time spoof detection— and yes, the model isincluded in the course for free!

What You'll Learn:

What liveness detection is and why it’s important

Real-world threats like photo, video, and mask spoofing

How to run a free TFLite spoof detection model on live camera feed

How to add liveness detection to an existing face recognition app

How to build fully offline, secure Android AI features

How to test and evaluate spoof detection in real time

Who Should Take This Course?

Android developers building camera-based or security apps

Developers who’ve built face recognition apps and want to upgrade them

Beginners — no prior ML experience needed

Anyone interested in mobile AI security features

Why You’ll Love It:

Includes complete real-time face recognition Android app

Free pre-trained TensorFlow Lite model for spoof detection

Works fully offline — no APIs, no cloud costs

Beginner-friendly and step-by-step

Builds a practical, in-demand mobile security feature

By the end, you’ll have a fully workingAndroid liveness detection system, integrated inside areal-time face recognition app, helping you secure your apps against spoofing — all without paying a cent for external services.

Who this course is for:
- App creators looking for a free and offline alternative to paid liveness detection APIs
- Students or professionals working on face-based login, attendance, or verification apps
- Developers creating apps for banking, eKYC, secure access, or identity verification
- Android developers who want to secure their apps with liveness (anti-spoofing) detection
- Anyone interested in mobile machine learning and using TensorFlow Lite in Android apps
More Info