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🛡️ FoodGuard

AI-Powered Food Fraud Detection System

📄 Read the Full Technical Report

Detect AI-generated, compressed, and tampered food images using deep learning forensics.

📌 Overview

FoodGuard is a 4-class deep learning system that classifies food images as:

Class Description
🟢 Real Genuine, unedited food photographs
🔴 Perfect AI High-quality AI-generated food images (no post-processing)
🟡 Compressed AI AI-generated images degraded by JPEG compression & resizing
🟠 Edited AI Real images tampered via AI inpainting (e.g., cockroach, mold inserted)

Goal: Achieve ≤ 5% False Positive Rate — genuine food photos must NOT be wrongly flagged.

🏗️ System Architecture

graph TB
    subgraph Data Collection
        K1["Kaggle: Food-101<br/>~101K images"]
        K2["Kaggle: Indian Food<br/>~4K images"]
        K3["Kaggle: Food Image Dataset<br/>~86K images"]
    end

    subgraph AI Generation
        G1["RealVisXL V4.0<br/>Text-to-Image"]
        G2["SDXL Inpainting<br/>Fraud Objects"]
    end

    K1 & K2 & K3 --> CSV["build_csv.py<br/>dataset_index.csv"]
    CSV --> ORG["organize_4class_dataset.py"]
    G1 --> C1["class1: Perfect AI<br/>600 imgs"]
    G1 --> C2["class2: Compressed AI<br/>600 imgs"]
    G1 --> C3["class3: Degraded AI<br/>400 imgs"]
    G2 --> C4["class4: Edited Real<br/>500+ imgs"]

    ORG --> DS["dataset_4class/<br/>train / val / test<br/>70% / 15% / 15%"]
    C1 & C2 & C3 & C4 --> DS

    DS --> TR["train_4class_detector.py<br/>EfficientNet-B3 + AMP"]
    TR --> CK["checkpoints/<br/>food_ai_detector.pth"]
    CK --> INF["inference.py<br/>Threshold-Calibrated Prediction"]

    style DS fill:#1a1a2e,stroke:#e94560,color:#fff
    style TR fill:#0f3460,stroke:#e94560,color:#fff
    style INF fill:#16213e,stroke:#00d2ff,color:#fff
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🔬 Training Pipeline

flowchart LR
    A["📂 Load Dataset<br/>ImageFolder"] --> B["🔄 Transforms<br/>512×512, Normalize"]
    B --> C["🧠 EfficientNet-B3<br/>Pretrained ImageNet"]
    C --> D["📉 Weighted CE Loss<br/>[1.2, 1.0, 1.0, 1.0]"]
    D --> E["⚡ AdamW + AMP<br/>lr=3e-4"]
    E --> F["📊 Cosine Scheduler<br/>20 Epochs"]
    F --> G{"Val Accuracy<br/>Improved?"}
    G -- Yes --> H["💾 Save Best Model"]
    G -- No --> I["Continue Training"]
    H --> J["🎯 Threshold Calibration<br/>Target FPR ≤ 5%"]
    J --> K["✅ Final Evaluation<br/>Confusion Matrix + Metrics"]
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🧬 Model Architecture

graph LR
    IMG["Input Image<br/>512 × 512 × 3"] --> EN["EfficientNet-B3<br/>~12M params"]
    EN --> GAP["Global Avg Pool<br/>1536-d"]
    GAP --> FC["Linear<br/>1536 → 4"]
    FC --> SM["Softmax"]
    SM --> R["P(real)"]
    SM --> P["P(perfect_ai)"]
    SM --> C["P(compressed_ai)"]
    SM --> E["P(edited_ai)"]

    R --> TH{"P(real) > θ ?"}
    TH -- Yes --> REAL["✅ REAL"]
    TH -- No --> AI["⚠️ AI Detected<br/>argmax of AI classes"]

    style REAL fill:#00c853,stroke:#00c853,color:#fff
    style AI fill:#ff1744,stroke:#ff1744,color:#fff
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🗂️ Project Structure

FoodGuard/
├── config/
│   └── category_mapping.yaml      # Food category mapping
├── src/
│   ├── data/
│   │   ├── dataset_loader.py      # Generic food dataset loader
│   │   ├── detector_dataset.py    # 4-class detector dataset
│   │   ├── augmentations.py       # Training transforms
│   │   └── ela.py                 # Error Level Analysis (forensic)
│   └── models/
│       ├── food_classifier.py     # Base food classifier
│       ├── dual_stream_detector.py# RGB + FFT dual-stream model
│       ├── focal_loss.py          # Focal Loss implementation
│       └── trainer.py             # Training loop manager
├── scripts/
│   ├── build_csv.py               # Build master dataset CSV
│   ├── build_detector_csv.py      # Build 4-class detector CSV
│   ├── organize_4class_dataset.py # Organize into train/val/test
│   ├── generate_ai_images.py      # AI food image generation (SDXL)
│   ├── generate_fraud_inpainting.py # Fraud object inpainting
│   ├── generate_fraud_simple.py   # Overlay-based fraud fallback
│   └── validate_csv.py            # Dataset validation checks
├── train_4class_detector.py       # 🚀 Main training script
├── evaluate.py                    # Model evaluation & metrics
├── inference.py                   # Single-image inference
├── requirements.txt               # Python dependencies
└── README.md

🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. 🪄 Run the Web App (Easiest Way!)

You don't need to train the model to try it! The trained model weights are already included in this repository.

As long as you have completed Step 1 (installing dependencies), you can just run the Streamlit app, upload a food photo, and watch the forensic AI do its magic ✨.

streamlit run app.py

It will automatically load the pre-trained weights. You can drag & drop images directly into your browser to see real-time forensic analysis, including Grad-CAM explainability and Error Level Analysis (ELA) bounding boxes.

3. Run Inference (Command Line)

If you prefer the terminal:

python inference.py path/to/food_image.jpg

4. Build Dataset & Train (For Developers)

If you want to train the model yourself or generate more AI images, you will use the files in the scripts/ folder.

Image Generation Scripts: All AI image generation happens in the scripts/ directory. If you want to generate your own fake food images, use scripts like generate_ai_images.py and generate_fraud_inpainting.py.

Prepare Data & Train:

# Build unified CSV and organize datasets
python scripts/build_csv.py
python scripts/organize_4class_dataset.py

# Run training (uses AMP automatically)
python train_4class_detector.py

Checkpoints will be saved to checkpoints/food_detector/.

Sample Output (Inference):

============================================================
Image: test_burger.jpg
============================================================
Prediction:  REAL
Confidence:  94.32%
Is Fake:     NO

Class Probabilities:
  real           :  94.32% █████████████████████████████████████████████
  perfect_ai     :   3.21% █
  compressed_ai  :   1.87% 
  edited_ai      :   0.60% 
============================================================

🎯 AI Image Generation

FoodGuard generates its own training data using RealVisXL V4.0 (SG161222/RealVisXL_V4.0):

graph TD
    subgraph "Text-to-Image Generation"
        P["Curated Food Prompts<br/>+ Quality Modifiers"] --> SDXL["RealVisXL V4.0<br/>25 steps, cfg=5-7.5"]
        SDXL --> RAW["Class 1: Raw AI<br/>512×512 PNG"]
        SDXL --> COMP["Class 2: Compressed<br/>JPEG q=40-85, resize"]
        SDXL --> DEG["Class 3: Degraded<br/>Blur + Noise"]
    end

    subgraph "Inpainting Generation"
        REAL["Real Food Image<br/>from clean pool"] --> MASK["Irregular Mask<br/>2-4% area, center-biased"]
        MASK --> INP["SDXL Inpainting<br/>cfg=4.5, strength=0.99"]
        INP --> EDIT["Class 4: Edited<br/>Fraud object inserted"]
    end

    style RAW fill:#e3f2fd,stroke:#1565c0,color:#000
    style COMP fill:#fff3e0,stroke:#e65100,color:#000
    style DEG fill:#fce4ec,stroke:#c62828,color:#000
    style EDIT fill:#ffebee,stroke:#b71c1c,color:#000
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Fraud Objects: cockroach, housefly, mosquito, bee, ant, worm, human hair, mold, plastic fragment, paper piece, metal shard.

📊 Data Sources

⚠️ Note: Due to GitHub's file size limits, the actual image datasets are NOT included in this repository.

🔗 [Download the AI-Generated Fake Food Dataset here (Kaggle / Google Drive)] (Link to be added)

Dataset Source Images Cuisine Coverage
Food-101 Kaggle (ETH Zurich) ~101,000 Western, International
Indian Food Dataset Kaggle ~4,000 Indian (biryani, paneer, etc.)
Food Image Dataset Kaggle (UECFOOD256 + AIcrowd) ~86,000 Japanese, Mixed
AI-Generated (Perfect & Compressed) See link above ~16,173 5-6 Models (RealVisXL, Flux, SDXL, Cascade, Kandinsky)
AI-Inpainted Fraud (Edited) See link above ~8,000 SDXL Inpainting

Total Real Images: ~191,000+  |  Final Combined Dataset: 36,173 images (Train/Val/Test = 70/15/15)

⚙️ Training Configuration

Parameter Value Rationale
Backbone EfficientNet-B3 Best accuracy-per-param; fits 12GB VRAM
Image Size 512 × 512 Preserves forensic artifacts vs 224×224
Batch Size 16 Max for 512×512 on 12GB
Optimizer AdamW Better weight decay for fine-tuning
Learning Rate 3e-4 Standard for timm fine-tuning
Scheduler Cosine Annealing (T=20) Smooth decay, no sudden drops
Loss Cross-Entropy Weights: [1.2, 1.0, 1.0, 1.0] — penalizes FP on real
AMP Enabled ~2× speed, ~40% less VRAM
Epochs 20 With early stopping
Target FPR ≤ 5% Calibrated via threshold sweep

🧪 Why 4 Classes, Not Binary?

graph TD
    B["Binary Classifier<br/>Real vs Fake"] --> L1["❌ Loses WHY it's fake"]
    B --> L2["❌ Misses subtle edits<br/>95% real, 5% inpainted"]

    F["4-Class Classifier"] --> W1["✅ Detects fully AI-generated"]
    F --> W2["✅ Handles compressed AI<br/>social media sharing"]
    F --> W3["✅ Catches subtle inpainting<br/>deliberate fraud"]
    F --> W4["✅ Maintains low FPR<br/>on genuine photos"]

    style B fill:#ffcdd2,stroke:#c62828,color:#000
    style F fill:#c8e6c9,stroke:#2e7d32,color:#000
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📈 Final Training Results

flowchart TD
    subgraph Dataset["📦 Final Dataset (36,173 images)"]
        T["Train — 25,321"]
        V["Val — 5,425"]
        TS["Test — 5,427"]
    end

    subgraph Journey["🏋️ DGX Kubernetes Training (20 Epochs)"]
        E1["Epoch 1\nVal Acc: 82.10%\nFPR: 8.40%"]
        E_Mid["Epoch 7\nVal Acc: 94.50%\nFPR: 3.20%"]
        E_Best["⭐ Epoch 14 — Best\nVal Acc: 96.26%\nFPR: 1.67%"]
    end

    subgraph Calibration["🎯 Threshold Calibration"]
        TH["Optimal Threshold: 0.50\nFPR on Real: 1.67%\nTarget met: ≤ 5% ✓"]
    end

    subgraph TestResults["✅ Test Set Results (5,427 images)"]
        direction LR
        ACC["Accuracy\n96.26%"]
        FPR["False Positive Rate\n1.67% 🎯"]
    end

    subgraph CM["📊 Test Split Breakdown"]
        R["Real\n1800 imgs"]
        P["Perfect AI\n1677 imgs"]
        C["Compressed AI\n750 imgs"]
        E["Edited AI\n1200 imgs"]
    end

    Dataset --> Journey
    E1 --> E_Mid --> E_Best
    E_Best --> Calibration
    Calibration --> TestResults
    TestResults --> CM

    style E_Best fill:#00c853,stroke:#00c853,color:#fff
    style FPR fill:#00c853,stroke:#00c853,color:#fff
    style ACC fill:#0096ff,stroke:#0096ff,color:#fff
    style TH  fill:#1a1a2e,stroke:#00c853,color:#fff
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🗺️ Roadmap

  • Real food data collection (3 Kaggle datasets, ~191K images)
  • AI image generation pipeline (RealVisXL V4.0)
  • Fraud inpainting pipeline (SDXL Inpainting)
  • Data processing & 4-class organization
  • Training script with AMP and threshold calibration
  • DGX Kubernetes Training Pipeline Setup (PVC, MIG GPU)
  • Executed model training on DGX Server
  • Threshold calibration for ≤5% FPR
  • Evaluation (confusion matrix, per-class metrics)
  • Grad-CAM explainability visualization (Planned)
  • Dual-stream model (RGB + FFT frequency analysis) (Planned)
  • REST API deployment (FastAPI) (Planned)

🔧 Tech Stack

Technology Purpose
PyTorch Deep learning framework
timm EfficientNet model zoo
HuggingFace Diffusers SDXL text-to-image & inpainting
RealVisXL V4.0 Photorealistic image generation
scikit-learn Metrics & evaluation
Pillow Image I/O and processing
xformers Memory-efficient attention for SDXL
CUDA AMP Mixed-precision training
matplotlib / seaborn Visualization

🌍 Real-World Applications

  • Food Delivery Apps — Detect fraudulent complaint images (fake contaminants for refunds)
  • Restaurant Reviews — Filter AI-manipulated food photos
  • Food Safety Agencies — Verify authenticity of food complaint evidence
  • Social Media — Flag AI-generated food content for transparency

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