Duplicate from AI-Safeguard/Ivy-Fake

Co-authored-by: Ivy Zhang <Ivy1997@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- AIGC
+size_categories:
+- 100K<n<1M
+---
+# IVY-FAKE: Unified Explainable Benchmark and Detector for AIGC Content
+
+[![Paper](https://img.shields.io/badge/paper-OpenReview-B31B1B.svg)](https://openreview.net/attachment?id=RIBj1KPAWM&name=pdf)
+[![Hugging Face Datasets](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Datasets-blue)](https://huggingface.co/datasets/AI-Safeguard/Ivy-Fake)
+[![GitHub Code](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/Pi3AI/IvyFake) [![License: CC BY-SA 4.0](https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg)](http://creativecommons.org/licenses/by-sa/4.0/)
+
+![Intro-image](figure1-poster-v2_00.png)
+
+This repository provides the official implementation of **IVY-FAKE** and **IVY-xDETECTOR**, a unified explainable framework and benchmark for detecting AI-generated content (AIGC) across **both images and videos**.
+
+---
+
+## 🔍 Overview
+
+**IVY-FAKE** is the **first large-scale dataset** designed for **multimodal explainable AIGC detection**. It contains:
+- **150K+** training samples (images + videos)
+- **18.7K** evaluation samples
+- **Fine-grained annotations** including:
+  - Spatial and temporal artifact analysis
+  - Natural language reasoning (<think>...</think>)
+  - Binary labels with explanations (<conclusion>real/fake</conclusion>)
+
+**IVY-xDETECTOR** is a vision-language detection model trained to:
+- Identify synthetic artifacts in images and videos
+- Generate **step-by-step reasoning**
+- Achieve **SOTA performance** across multiple benchmarks
+
+---
+
+## 📦 Evaluation
+
+```bash
+conda create -n ivy-detect python=3.10
+conda activate ivy-detect
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+---
+
+🚀 Evaluation Script
+
+We provide an evaluation script to test large language model (LLM) performance on reasoning-based AIGC detection.
+
+🔑 Environment Variables
+
+Before running, export the following environment variables:
+
+```bash
+export OPENAI_API_KEY="your-api-key"
+export OPENAI_BASE_URL="https://api.openai.com/v1"  # or OpenAI's default base URL
+```
+
+▶️ Run Evaluation
+
+```bash
+python eva_scripts.py \
+  --eva_model_name gpt-4o-mini \
+  --res_json_path ./error_item.json
+```
+
+This script compares model predictions (<conclusion>real/fake</conclusion>) to the ground truth and logs mismatches to error_item.json.
+
+---
+
+🧪 Input Format
+
+The evaluation script `res_json_path` accepts a JSON array (Dict in List) where each item has:
+```json
+{
+  "rel_path": "relative/path/to/file.mp4",
+  "label": "real or fake",
+  "raw_ground_truth": "<think>...</think><conclusion>fake</conclusion>",
+  "infer_result": "<think>...</think><conclusion>real</conclusion>"
+}
+```
+
+- label: ground truth
+- raw_ground_truth: reasoning by gemini2.5 pro
+- infer_result: model reasoning and prediction
+
+Example file: `./evaluate_scripts/error_item.json`
+
+---

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