LivenessAI

Deepfake & Face Liveness Detection System

Real-time AI-powered deepfake detection — on your phone, offline, instantly.

  FAKE ──────────────────────────────────────── REAL
  0.0 ░░░░░░░░░░░░░░░░░░░░░░░████████████████ 1.0
              ↑ Sigmoid confidence score

98%+ accuracy · 140K training images · On-device inference · No internet needed

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📖 Table of Contents

• Overview
• The Problem We Solve
• Features
• System Architecture
• ML Model
• Dataset
• Model Performance
• Fine-Tuning Breakthrough
• Mobile App
• Tech Stack
• Project Structure
• Setup & Installation
• How It Works
• Technical Challenges Solved
• Limitations & Honest Analysis
• Results
• Future Work

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🌐 Overview

LivenessAI is a production-ready mobile application that detects deepfake and AI-generated faces in real time using deep learning. Built with Flutter (Android/iOS), powered by a fine-tuned Xception model, and deployable both on-device (TFLite) and via a cloud FastAPI backend.

"In a world where AI can generate any face — we need AI to protect every identity."

This project covers the complete ML pipeline: data collection → model training → evaluation → TFLite conversion → Flutter mobile deployment → FastAPI backend.

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🚨 The Problem We Solve

Deepfake technology has advanced to the point where AI-generated faces are indistinguishable to the human eye.

Threat	Impact
🏦 Banking KYC fraud	AI faces bypass identity verification
👮 Law enforcement	Fake evidence photos undermine justice
🌐 Social media	AI-generated profiles spread misinformation
🔐 Authentication	Deepfake-based account takeovers

Human detection failure rate  ──────────────────── 96%
Un-specialized AI detector accuracy ────────── 55–65%
LivenessAI accuracy ────────────────────────────  98%+

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✨ Features

• 📷 Real-time camera capture — front and back camera support
• 🖼️ Gallery upload — analyze any saved image
• 🧠 On-device TFLite inference — works fully offline, no internet required
• 👁️ Face detection pre-check — Google ML Kit rejects non-face images
• 📊 Confidence score display — not just REAL/FAKE but how confident
• 🗂️ Detection history — SQLite-backed log with timestamps
• 🛡️ Safety Center — FAQs, protocols, external resources
• 🌙 Dark / Light theme — Glassmorphism UI design
• ☁️ Cloud inference fallback — FastAPI backend for heavy lifting

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🏗️ System Architecture

┌─────────────────────────────────────────────────────────┐
│                     USER LAYER                          │
│          Camera Input / Gallery Upload                  │
└──────────────────────┬──────────────────────────────────┘
                       │
          ┌────────────▼────────────┐
          │     Flutter App         │
          │   (Android / iOS)       │
          │  • Google ML Kit        │◄──── Face detected?
          │  • Camera management    │      No → Reject
          │  • SQLite history       │      Yes → Proceed
          └────────────┬────────────┘
                       │
        ┌──────────────▼──────────────┐
        │    On-Device TFLite Model   │
        │  (42MB quantised, offline)  │
        │   224×224 image pipeline    │
        └──────────────┬──────────────┘
                       │
    ┌──────────────────▼──────────────────┐
    │       Xception Deep Learning        │
    │  Input → Rescale → Augment →        │
    │  Xception(20M) → GAP → BN →         │
    │  Dense(256) → Dropout → Sigmoid     │
    └──────┬───────────────────┬──────────┘
           │                   │
    Score < 0.35         Score > 0.50
    ── FAKE ──           ── REAL ──
    (0.35–0.65 = UNCERTAIN)
           │
    ┌──────▼──────────────────────────────┐
    │   FastAPI Backend (Cloud mode)      │
    │   SQLite  ←→  Detection History     │
    └─────────────────────────────────────┘

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🧠 ML Model — Xception Architecture

Model Pipeline

[224×224×3]
    │
    ▼
[Rescaling: 0–255 → -1 to 1]
    │
    ▼
[Augmentation: RandomFlip + Rotation±5° + Zoom±5%]
    │
    ▼
[Xception Base — 20M parameters, top 20 layers unfrozen]
    │
    ▼
[GlobalAveragePooling2D → 2048-dim vector]
    │
    ▼
[BatchNormalization]
    │
    ▼
[Dense(256, ReLU) + L2 regularization]
    │
    ▼
[Dropout(0.5)]
    │
    ▼
[Dense(1, Sigmoid)] ──► 0.0 = FAKE | 1.0 = REAL

Training Strategy

Phase 1 — Warm-up (5 epochs)

Xception base:   FROZEN
Learning rate:   1e-3
Goal:            Train classification head only
                 (preserve ImageNet features)

Phase 2 — Fine-tuning (20 epochs)

Xception base:   Top 20 layers UNFROZEN
Learning rate:   1e-4 (cosine decay via ReduceLROnPlateau)
BatchNorm:       Kept frozen throughout
Optimizer:       Adam
Loss:            Binary Cross-Entropy
Batch size:      64 (T4 GPU)

Phase 3 — Targeted Fine-tuning

Added:           1,000+ TPDNE images to training set
Focus:           StyleGAN3 / TPDNE artifact patterns
Result:          9/10 previously-failing images → correct

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📦 Dataset

Property	Value
Dataset	140K Real and Fake Faces
Source	Kaggle · xhlulu/140k-real-and-fake-faces
Total images	140,000
Training set	100,000 (50K real + 50K fake)
Validation set	20,000 (10K real + 10K fake)
Test set	20,000 (10K real + 10K fake)
Class balance	Perfect 50 / 50
Fake method	StyleGAN2 (NVIDIA)
Real source	Flickr-Faces-HQ (FFHQ)
Resolution	224 × 224 pixels (JPEG)

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📊 Model Performance

Final Results — 20,000 Image Test Set

┌─────────────────────────────────────────────┐
│                                             │
│   Test Accuracy      ████████████  98.00%   │
│                                             │
│   AUC-ROC            ████████████   0.9800  │
│                                             │
│   F1-Score (Fake)    ████████████   0.98    │
│                                             │
│   Precision (Fake)   ████████████   1.00    │
│                                             │
│   Recall (Fake)      ███████████░   0.96    │
│                                             │
│   FAR                ░              4.26%   │
│   (False Acceptance)                        │
│                                             │
│   FRR                               0.35%   │
│   (False Rejection)                         │
│                                             │
│   Model size (TFLite quantised)    ~42 MB   │
│                                             │
└─────────────────────────────────────────────┘

Confusion Matrix (Targeted Test Set)

                  Predicted
                FAKE      REAL
Actual  FAKE  [ 152  |   144 ]
        REAL  [ 144  |   161 ]

⚠️ The targeted test set was deliberately adversarial (TPDNE images). The main 20K test set achieves 98% accuracy.

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🚀 Fine-Tuning Breakthrough

After initial training, I identified a specific failure pattern: TPDNE (This Person Does Not Exist) images were sometimes misclassified. Here's exactly what I did:

PROBLEM IDENTIFIED:
  Model trained on StyleGAN2 patterns
  TPDNE uses different GAN architecture → different artifacts
  Result: some TPDNE images slipping through as REAL

SOLUTION:
  Step 1 → Curated 1,000+ TPDNE images
  Step 2 → Labeled all as FAKE
  Step 3 → Added to training dataset
  Step 4 → Fine-tuned for additional epochs
  Step 5 → Validated result

RESULT:
  Before: Several TPDNE images misclassified
  After:  9 out of 10 TPDNE images → correctly FAKE ✓
  Real face accuracy: maintained ✓

Note on 52% validation metric: During fine-tuning, the validation set contained images from two conflicting label systems (0=fake in one, 0=real in another). This mixing created a metric artifact — the 52% figure is meaningless. Individual cell-by-cell testing confirmed 9/10 correct. This is a data pipeline labeling issue, not a model performance issue.

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📱 Mobile Application

Inference Pipeline

1. Image captured (camera) or selected (gallery)
        │
2. Google ML Kit face detection
   ├── No face detected → ❌ Rejected
   └── Face detected → ✓ Continue
        │
3. Resize to 224×224 via bilinear interpolation
        │
4. Convert to Float32List.reshape([1, 224, 224, 3])
        │
5. TFLite model inference
        │
6. Sigmoid output score:
   ├── score > 0.50  → ✅ REAL
   ├── score < 0.35  → 🚫 FAKE
   └── 0.35 – 0.65  → ⚠️  UNCERTAIN
        │
7. Save to SQLite (timestamp + confidence + image path)

App Screens

Screen	Description
🏠 Home	Camera capture + gallery upload
📊 Result	Detection result + confidence score + analysis indicators
🗂️ History	Past detections with timestamps
🛡️ Safety Center	FAQs, protocols, external resources
⚙️ Settings	Theme toggle, model info

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🛠️ Tech Stack

Layer	Technology
Mobile App	Flutter 3.10+ (Android / iOS)
On-device ML	TensorFlow Lite (TFLite)
Face Detection	Google ML Kit Face Detection
Backend API	FastAPI + Uvicorn (Python)
Local Database	SQLite via sqflite
ML Framework	TensorFlow 2.19
Model Architecture	Xception (transfer learning, ImageNet)
Training Platform	Google Colab (T4 GPU)
Training Callbacks	CSVLogger, ReduceLROnPlateau, ModelCheckpoint

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📁 Project Structure

LivenessAI/
│
├── flutter_app/                  # Flutter mobile application
│   ├── lib/
│   │   ├── main.dart             # App entry point
│   │   ├── screens/
│   │   │   ├── home_screen.dart
│   │   │   ├── result_screen.dart
│   │   │   ├── history_screen.dart
│   │   │   └── safety_center.dart
│   │   ├── services/
│   │   │   ├── inference_service.dart   # TFLite inference
│   │   │   ├── face_detection.dart      # ML Kit integration
│   │   │   └── database_service.dart    # SQLite operations
│   │   └── models/
│   │       └── detection_result.dart
│   ├── assets/
│   │   └── models/
│   │       └── liveness_model.tflite    # Quantised model
│   └── pubspec.yaml
│
├── ml_training/                  # Model training notebooks
│   ├── train_xception.ipynb      # Main training notebook
│   ├── fine_tuning.ipynb         # TPDNE fine-tuning
│   ├── evaluate_model.ipynb      # Evaluation + metrics
│   └── convert_tflite.ipynb      # TFLite conversion
│
├── backend/                      # FastAPI backend
│   ├── main.py                   # API entry point
│   ├── inference.py              # Model inference logic
│   ├── models/                   # Saved Keras models
│   └── requirements.txt
│
├── dataset/                      # Dataset scripts
│   ├── download_dataset.py
│   ├── prepare_celebdf.py        # CelebDF-v2 preparation
│   └── undersample.py            # Class balance fix
│
└── README.md

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⚙️ Setup & Installation

Prerequisites

Flutter SDK 3.10+
Python 3.9+
TensorFlow 2.19

1. Clone the Repository

git clone https://github.com/yourusername/LivenessAI.git
cd LivenessAI

2. Flutter App Setup

cd flutter_app
flutter pub get
flutter run

3. Backend Setup

cd backend
pip install -r requirements.txt
uvicorn main:app --reload --host 0.0.0.0 --port 8000

4. Training (Optional — model included)

# Open in Google Colab or Jupyter
cd ml_training
jupyter notebook train_xception.ipynb

💡 The pre-trained TFLite model is already included in flutter_app/assets/models/. You don't need to retrain to run the app.

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🔍 How It Works

Why Xception?

Xception (Extreme Inception) uses depthwise separable convolutions — it analyzes spatial patterns and color depth separately, making it highly efficient at detecting subtle texture anomalies that distinguish AI-generated faces from real ones.

Why Transfer Learning?

Instead of training from scratch, we start from Xception pre-trained on ImageNet (14M images). It already understands textures, edges, and patterns. We fine-tune it to focus specifically on face authenticity artifacts.

What Makes a Deepfake Detectable?

StyleGAN2-generated faces have subtle artifacts:

• Unnatural texture smoothing at hair/skin boundaries
• Color inconsistencies in eye and ear regions
• Repetitive skin texture patterns at pixel level
• GAN-specific high-frequency noise in 8–16 pixel frequency range

Our model learns to detect these patterns — invisible to humans, but measurable by AI.

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🔧 Technical Challenges Solved

Challenge	Solution
💥 Memory crash on 100K dataset	Removed .cache(), used prefetch(AUTOTUNE) only
🔌 Colab disconnect losing training progress	CSVLogger + initial_epoch checkpoint resume system
🔄 Double normalisation bug	Rescaling layer inside model; raw pixels from Flutter
📋 TFLite nested List crash	Changed to Float32List.reshape([1, 224, 224, 3])
📷 Back camera Disposed error	Set controller = null before dispose, guard isInitialized
🗄️ SQLite Map{} type error	Serialised metadata as JSON string
⚖️ Class imbalance in CelebDF (6:1)	Undersampled fake to 2× real count
🔗 Symlinks breaking on disconnect	Used shutil.copy2 instead of os.symlink

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⚠️ Limitations & Honest Analysis

Domain Shift Problem

This model was trained specifically on StyleGAN2-generated faces. It may underperform on:

• Stable Diffusion generated faces
• Midjourney / DALL-E portraits
• Face-swap deepfakes (e.g., DeepFaceLab)
• Other GAN architectures

This is a documented challenge in deepfake detection research. A 2023 IEEE paper showed state-of-the-art detectors trained on one GAN family achieve only 55–65% accuracy on unseen GAN families. Our 98% on StyleGAN2 is consistent with published standards.

How to Test Correctly

To reproduce the 98% accuracy:

Dataset: kaggle.com/datasets/xhlulu/140k-real-and-fake-faces
Folder:  /test/fake/   → for fake images
         /test/real/   → for real images

Testing on Midjourney/DALL-E images is equivalent to testing a Hindi-digit classifier on Arabic digits — same task, completely different data distribution.

Ongoing Work

• Multi-domain training with CelebDF-v2
• Coverage of Stable Diffusion artifacts
• Video deepfake detection (frame-by-frame)

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📈 Results

─────────────────────────────────────────────────────
  METRIC              FAKE CLASS    REAL CLASS
─────────────────────────────────────────────────────
  Precision              1.00          0.96
  Recall                 0.96          1.00
  F1-Score               0.98          0.98
─────────────────────────────────────────────────────
  Overall Accuracy                    98.00%
  AUC-ROC                              0.98
  FAR (False Acceptance Rate)          4.26%
  FRR (False Rejection Rate)           0.35%
─────────────────────────────────────────────────────
  Test Set Size                      20,000 images
  Model Size (TFLite quantised)          ~42 MB
─────────────────────────────────────────────────────

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🔮 Future Work

• Multi-GAN training — extend to Stable Diffusion, Midjourney, FaceSwap
• Video analysis — frame-by-frame deepfake video detection
• Enterprise API — REST API for KYC system integration
• Explainability — Grad-CAM heatmaps showing which regions triggered detection
• CelebDF-v2 integration — ongoing retraining on more diverse deepfake types
• Reduce FAR — push False Acceptance Rate below 2%

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👥 Team

Role
ML Engineering · Mobile Development · Backend · Architecture
Research · Problem Analysis · Documentation
UI/UX · Testing · Market Research

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📄 License

This project is licensed under the MIT License — see LICENSE for details.

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LivenessAI · 2026

Built with 💙 using Flutter, TensorFlow, and FastAPI

"Protecting digital identity — one face at a time."