README.md

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title: TrueLens Forensics
emoji: 👁️
colorFrom: gray
colorTo: gray
sdk: docker
app_file: app.py
pinned: false

TrueLens Forensic Suite v1.0

Advanced Multimedia Authenticity Verification System

TrueLens is a professional-grade forensic suite designed to detect AI-generated synthetic media and deepfakes with high precision. By combining state-of-the-art Neural Networks (SigLIP2) with classical digital forensic signals (ELA, FFT, DCT), TrueLens provides a multi-layered defense against generative misinformation.

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🏛️ System Architecture

TrueLens employs a Hybrid Forensic-Neural Ensemble (HFNE) architecture:

1. Neural Analysis Layer

• Primary Classifier: SigLIP2 (Vision Transformer) fine-tuned for synthetic artifact detection.
• Strategy: Multi-crop Test-Time Augmentation (TTA) focusing on facial biometrics, central composition, and global structure.

2. Forensic Signal Layer (Classical)

• DCT Block Analysis: Detects inconsistent JPEG grid alignments and compression quantization typical of authentic camera hardware.
• ELA (Error Level Analysis): Identifies non-uniform compression levels across different regions of an image.
• FFT (Fast Fourier Transform): Scans the frequency spectrum for high-frequency checkerboard artifacts unique to GANs and Diffusion models.
• Noise Consistency: Analyzes pixel-level sensor noise variance to detect synthetic patches.
• Edge Sharpness: Monitors gradient uniformity to catch unnaturally sharp or blurred synthetic edges.

3. Consensus Fusion Logic

A weighted Bayesian logic gate synthesizes the output of all layers. It includes an Uncertainty Quantification engine that flags ambiguous samples rather than providing false positives, ensuring the system's "Forensic Integrity."

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🚀 Performance Benchmarks

Evaluated against a controlled dataset of 66 high-resolution samples (Pexels realistic photos vs. SDXL/Midjourney synthetic generations):

Metric	Local Engine (CPU)
Accuracy	92.4%
Precision	0.94
Recall	0.91
F1-Score	0.92
Avg. Latency	~2.2s

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🛠️ Installation & Setup

Prerequisites

• Python 3.10 or higher
• pip (Python package manager)

Quick Start

1. Clone the repository:

   git clone https://github.com/abhiisonu/TrueLens.git
   cd TrueLens
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Initialize the engine:

   python app.py
   

   Note: On first run, it will download approximately 800MB of neural weights.

4. Access the Workbench: Open http://localhost:5006 in your browser.

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📊 Forensic Evaluation

You can run the batch evaluator to verify the engine's performance on your own datasets:

python evaluate.py --fake-dir "path/to/fakes" --real-dir "path/to/reals"

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⚖️ Privacy & Disclosure

• Privacy first: TrueLens processes all inference on-device.
• Zero-Persistence: Uploaded samples are purged from memory and disk immediately after inference completion.
• Disclosure: This tool is an academic project designed for forensic research. While highly accurate, forensic signals can be bypassed by advanced adversarial techniques (anti-forensics).

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📜 Credits & License

Developed as a Minor Project for Forensic Research. Licensed under the MIT License.

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© 2026 TrueLens Forensic Lab. All rights reserved.