README.md

metadata

title: VeriDex
emoji: 🛡️
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 4.19.2
app_file: app.py
pinned: false
short_description: Multimodal Claim Verification Engine

VeriDex: A Fake News Detection System Using Text Classification and Image Forensics

Author: Rakesh Kumar Raut

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📖 Abstract & Overview

AI-generated misinformation has exploded, and automated fact-verification systems are struggling to keep up. Modern Large Language Models (LLMs) churn out text that is almost impossible to tell apart from real language, while diffusion-based image synthesis creates deepfakes so realistic they make false claims look credible. These combined threats create a crucial "Zero-Day window" right after a big event, where independent fact-checks are missing, and evidence-based systems are helpless.

VeriDex (Verified Deception Index) is a Hybrid Multi-Modal Credibility Assessment System designed to tackle text from LLMs, AI-generated images, and the Zero-Day problem in one principled and transparent package.

VeriDex runs three independently trained deep-learning pipelines and brings their results together through a mathematically derived Hybrid Resolution Engine. Tested on a custom 500-claim adversarial benchmark, VeriDex achieves state-of-the-art results, significantly outperforming text-only and retrieval-only baselines.

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🏗️ Core Architecture (The Three Pipelines)

VeriDex takes a modular approach, with three parallel pipelines feeding into a central Hybrid Resolution Engine. This complementary redundancy ensures that each pipeline thrives exactly where the others fall short.

1. Pipeline A: Linguistic Deception Analyzer (HierFND)

Catches linguistic deception based on writing style and text artifacts.

• Model: Fine-tuned RoBERTa-base sequence classifier.
• Training Data: LIAR, ISOT, and WELFake datasets.
• Output: A Linguistic Risk Score ($R_{ling}$) reflecting the probability of the text being machine-generated or stylistically deceptive.

2. Pipeline B: Retrieval-Augmented Stance Aggregator (StanceFormer)

Verifies claims against actual evidence via real-time web retrieval.

• Model: Fine-tuned DeBERTa-v3-base Natural Language Inference (NLI) model.
• Mechanism: The claim triggers a web search API, pulling the top $k=10$ articles.
• Domain-Credibility RAG: Each article’s domain gets mapped to a Media Bias/Fact Check (MBFC) credibility multiplier (1.5 for high-credibility, 1.0 for neutral, 0.2 for questionable/satire).
• Output: An Evidence Consensus Score ($E_{pro}$) indicating the factual support for the claim.

3. Pipeline C: AI Image Forensics (CRAFT)

Pinpoints AI-generated images (deepfakes).

• Architecture: Combines a CLIP ViT-B/32 semantic branch with LoRA adapters and a 2D FFT-based FrequencyBranch.
• Mechanism: Merges 512-dimensional semantic features with 128-dimensional spectral features to catch checkerboard artifacts from GANs and diffusion models.
• Output: Image forensic classification (AI-Generated vs. Authentic) powered by a two-layer MLP head.

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⚙️ Hybrid Resolution Engine

The core blend mixes linguistic risk with evidence-based risk using an empirically derived blending weight ($\alpha^* = 0.40$):

$$R_{total}=(R_{ling}\times 0.40)+((1-E_{pro})\times 0.60)R\_\{total\}\; =\; (R\_\{ling\}\; \backslash times\; 0.40)\; +\; ((1\; -\; E\_\{pro\})\; \backslash times\; 0.60)$$

This 40/60 split keeps pure text classifiers from missing LLM-generated fakes, while also countering the failures of retrieval-only approaches during the Zero-Day window.

Fail-Safe Override Mechanisms

Two formal override rules ensure system robustness:

1. Explicit Debunk Override: If a highly credible article (weight $\ge 1.4$) strongly refutes the claim and uses terms like "fact check", "debunked", or "false", the system forces $R_{total} \leftarrow \max(R_{total}, 0.90)$. This stops a clever lie from outweighing legitimate fact-checking.
2. Image Forensics Override: If an image is flagged as AI-Generated with $\ge 85%$ confidence, the system forces $R_{total} \leftarrow \max(R_{total}, 0.85)$.

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📊 Empirical Results & Benchmarks

VeriDex was rigorously evaluated on a custom 500-claim adversarial benchmark encompassing Standard Fake News, Standard Real News, Zero-Day Claims, and LLM-Generated Fakes.

Integrated System Performance

Metric	Score	Category Accuracy Breakdown
Accuracy	94.80%	Standard Fake: 97.5%
AUC-ROC	0.965	Standard Real: 98.0%
Precision (Fake)	0.9751	Zero-Day: 89.3%
Recall (Fake)	0.9352	LLM-Generated: 86.7%
Macro-F1	0.9468

Comparison to State-of-the-Art (SOTA): VeriDex beats FakeBERT by +4.8 pp, VeraCT Scan by +3.3 pp, and classical TF-IDF+SVM approaches by +12.8 pp.

Image Forensics (CRAFT) Performance

Tested on a rigorous cross-generator protocol against 50,000 images (DALL-E 2, Midjourney v5, ProGAN, StyleGAN).

• In-Distribution Accuracy: 91.5%
• Cross-Generator Accuracy: 84.3% (Outperforming GenDet CVPR 2024 by 4.9 pp)
• AUC-ROC: 0.921

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🌟 Novel Contributions

1. Empirical 40/60 Weighting: The first systematic grid search (101 configurations) over blending weights for linguistic and evidence information, backed by a bootstrapped 95% confidence interval [0.340, 0.462].
2. Domain-Credibility RAG: MBFC credibility multipliers act as continuous weights in evidence aggregation, boosting accuracy by +1.8 pp and blocking echo-chamber manipulation.
3. Explicit Debunk Override: A formal fail-safe ensuring high-credibility explicit fact-checks cannot be diluted by text model confidence (+1.4 pp accuracy).
4. CLIP + LoRA + FrequencyBranch: A novel parameter-efficient deepfake detector that slashes generalization degradation by 32% compared to CLIP-only setups.

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🚀 Setup & Installation

Prerequisites

• Python 3.9+
• CUDA-enabled GPU (Highly Recommended for inference speed)

1. Clone & Install

git clone https://github.com/RakeshRautDev/VeriDex.git
cd VeriDex
pip install -r requirements.txt

2. Environment Variables

Copy .env.example to .env inside VeriDex_WebApp and configure your API keys.

cd VeriDex_WebApp
cp .env.example .env

Edit .env to include your search API keys: TAVILY_API_KEY=your_key_here

3. Model Weights Setup

Due to GitHub's file size constraints (100MB max per file), heavy binary model weights are NOT included in this repository.

You must download the pre-trained weights separately and place them in their respective model directories:

• models/fakeNewsModel/pytorch_model.bin
• models/stanceModel/model.safetensors
• models/imageDetectionModel/best_model.pth

4. Run the Web Application

cd VeriDex_WebApp
uvicorn app:app --host 0.0.0.0 --port 8000 --reload

Open http://localhost:8000/static/index.html in your browser to access the verification dashboard.

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📝 License

MIT License