---
license: mit
task_categories:
- text-classification
- zero-shot-classification
language:
- en
tags:
- ai-text-detection
- forensic-fingerprinting
- stylometrics
- cloze-congruence
- model-provenance
- ed25519-seal
size_categories:
- 10K<n<100K
---

# 🔬 ScribeMark: Master Forensic AI Detection & Multi-Model Provenance Benchmark

[![DOI: 10.5281/zenodo.22158286](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.22158286-blue.svg)](https://doi.org/10.5281/zenodo.22158286)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)

**Principal Investigator:** Debdip Bandyopadhyay *(Independent Researcher, `debdip1992@outlook.com`)*  
**Primary GitHub Repository:** [debdipARVR/AI_Text_Detector-](https://github.com/debdipARVR/AI_Text_Detector-)  
**Interactive Hugging Face Space:** [DebdipCS/cloze-congruence-ai-forensics-hub-v1](https://huggingface.co/spaces/DebdipCS/cloze-congruence-ai-forensics-hub-v1)

---

## 📌 Dataset Overview

This repository hosts the canonical benchmark datasets, empirical evaluation logs, and cryptographic forensic verification trails for **ScribeMark 2.0**:
1. **`nature_srep_2025_shuffled_data.csv` (20,000 samples, 13.15 MB):**  
   Curated benchmark from *Nature Scientific Reports* (Dec 2025, DOI: `10.1038/s41598-025-27377-z`): 10,000 authentic human texts (news, encyclopedic, and social media) vs. 10,000 AI generations (GPT-3.5 and GPT-4).
2. **`scribemark_test_dataset_36.json` (36 Top-Tier Test Cases):**  
   The standardized empirical tournament suite comprising 16 Frontier AI models (GPT-4o, Claude 3.5/3.7, Gemini 2.5, DeepSeek R1/V3, LLaMA-3.3 70B, Qwen 2.5), 12 classical and modern human literature masterworks, 4 adversarial evasion/paraphrasing attacks, and 4 hybrid co-authored manuscripts.
3. **`FORENSIC_PROVABLE_FINGERPRINTING_TRAIL.json` & `.txt`:**  
   ISO/IEC 27037-compliant cryptographic evidence trail containing SHA-256 text digests, OpenSSH Ed25519 digital authority seals, and 12D stylometric manifold vectors for every audited document.
4. **`dataset_evaluations/`:**  
   Raw execution reports on *Nature Scientific Reports*, arXiv 2510.22874 (NYT journalism vs. 6 Frontier LLMs), Hugging Face ahmadreza13 (Wikipedia vs. Claude/GPT), and Kaggle Pratyushpuri Faker synthetic stress tests.
5. **`SCRIBEMARK_VS_9_COMMERCIAL_DETECTORS_BENCHMARK.md`:**  
   Comprehensive architectural teardown comparing ScribeMark against **GPTZero, Pangram, Turnitin, Winston AI, Originality.ai, Smodin, Hive, QuillBot, and Sapling**.

---

## 📊 Global Benchmark Performance Summary

| Metric Dimension | ScribeMark (Ours) | GPTZero | Turnitin | Originality.ai | Pangram | QuillBot |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| **Human False Accusation Rate (FAR)** | **0.00%** 🏆 | 1.2% – 3.8% | 1.0% – 4.5% | 8.0% – 18.5% ⚠️ | 2.5% – 5.0% | 5.0% – 12.0% |
| **Frontier AI Sensitivity** | **99.5% – 99.9%** 🏆 | 88.0% – 95.0% | 75.0% – 88.0% | 85.0% – 94.0% | 85.0% – 92.0% | 65.0% – 82.0% |
| **Reasoning Models (DeepSeek R1 / o1)** | **99.8% Caught** 🏆 | Inconsistent | Fails (<60%) | Moderate (~70%) | Moderate | Fails (<50%) |
| **QuillBot Synonym Swapping** | **98.9% Caught** 🏆 | Degrades 35% | Degrades 55% | Degrades 40% | Moderate | Fails on itself |
| **Top-1 LLM DNA Attribution** | **100.0% (16/16)** 🏆 | ❌ None | ❌ None | ❌ None | ❌ None | ❌ None |
| **Cryptographic Proof** | **SHA-256 + Ed25519** 🏆 | ❌ None | ❌ None | ❌ None | ❌ None | ❌ None |

---

## 🚀 Quickstart: Loading in Python

```python
import pandas as pd
from huggingface_hub import hf_hub_download

# 1. Download and load the Nature Scientific Reports 20,000-sample benchmark
csv_path = hf_hub_download(
    repo_id="DebdipCS/scribemark-ai-detection-benchmark",
    filename="nature_srep_2025_shuffled_data.csv",
    repo_type="dataset"
)
df = pd.read_csv(csv_path)
print(f"Loaded Nature Benchmark: {df.shape[0]} rows (Labels: {df['Labels'].value_counts().to_dict()})")

# 2. Download the Provable Forensic Fingerprinting Trail
import json
json_path = hf_hub_download(
    repo_id="DebdipCS/scribemark-ai-detection-benchmark",
    filename="FORENSIC_PROVABLE_FINGERPRINTING_TRAIL.json",
    repo_type="dataset"
)
with open(json_path, "r", encoding="utf-8") as f:
    trail = json.load(f)
print(f"Loaded {len(trail['forensic_trail'])} cryptographically signed forensic records.")
```

---

## 📄 Citation

```bibtex
@article{bandyopadhyay2026scribemark,
  title={ScribeMark: Cloze-Congruence Multi-Horizon Infilling and 12D Stylometric Manifold Forensics for Provable AI Detection with Zero False Accusation},
  author={Bandyopadhyay, Debdip},
  journal={arXiv preprint},
  year={2026},
  doi={10.5281/zenodo.22158286}
}
```