ImBD: AI-generated content detection to detect whether content is generated by artificial intelligence

ImBD (Imitate Before Detect) 是一个开创性的机器生成文本检测项目，该项目发表于AAAI 2025会议。随着ChatGPT等大语言模型(LLMs)的广泛应用，识别AI生成的文本内容变得越来越具有挑战性。ImBD项目提出了一种新颖的”先模仿后检测”方法，通过深入理解和模仿机器文本的风格特征来提升检测效果。该方法首次提出对齐机器文本的风格偏好，建立了一个全面的文本检测框架，能够有效识别经过人工修改的机器生成文本。项目采用Apache 2.0开源许可证，提供了完整的代码实现、预训练模型和详细文档，方便研究人员和开发者在此基础上进行进一步的研究和应用开发。

Demo address: https://ai-detector.fenz.ai/ai-detector

Function List

• Supports high-precision detection of machine-generated text
• Provide pre-trained models for immediate deployment and use
• Novel textual style feature alignment algorithm implemented
• Includes detailed experimental datasets and evaluation benchmarks
• Provide complete training and inference code
• Supports customized training data for model fine-tuning
• Includes detailed API documentation and usage examples
• Provides command line tools for quick testing and evaluation
• Supports batch text processing
• Includes visualization tools to display test results

Using Help

1. Environmental configuration

First you need to configure your Python environment and install the necessary dependencies:

git clone https://github.com/Jiaqi-Chen-00/ImBD
cd ImBD
pip install -r requirements.txt

2. Data preparation

Before starting to use ImBD, training and test data need to be prepared. The data should contain the following two categories:

• Manually prepared original text
• Machine-generated or machine-modified text

Data format requirements:

• Text files need to be UTF-8 encoded
• Each sample takes up one row
• It is proposed to divide the dataset into training set, validation set and test set in the ratio of 8:1:1

3. Model training

Run the following command to start training:

python train.py \
--train_data path/to/train.txt \
--val_data path/to/val.txt \
--model_output_dir path/to/save/model \
--batch_size 32 \
--learning_rate 2e-5 \
--num_epochs 5

4. Model evaluation

Evaluate model performance using test sets:

python evaluate.py \
--model_path path/to/saved/model \
--test_data path/to/test.txt \
--output_file evaluation_results.txt

5. Text detection

Detection of individual texts:

python detect.py \
--model_path path/to/saved/model \
--input_text "要检测的文本内容" \
--output_format json

Batch detection of text:

python batch_detect.py \
--model_path path/to/saved/model \
--input_file input.txt \
--output_file results.json

6. Advanced functions

6.1 Model fine-tuning

If you need to optimize for domain-specific text, you can fine-tune the model using your own dataset:

python finetune.py \
--pretrained_model_path path/to/pretrained/model \
--train_data path/to/domain/data \
--output_dir path/to/finetuned/model

6.2 Visualization analysis

Use the built-in visualization tools to analyze the test results:

python visualize.py \
--results_file path/to/results.json \
--output_dir path/to/visualizations

6.3 API Service Deployment

Deploy the model as a REST API service:

python serve.py \
--model_path path/to/saved/model \
--host 0.0.0.0 \
--port 8000

7. Caveats

• It is recommended to use GPUs for model training to improve efficiency
• Training data quality has a significant impact on model performance
• Regularly update the model to accommodate new AI-generated text features
• Pay attention to model versioning when deploying in production environments
• It is recommended to save the test results for subsequent analysis and model optimization

8. Frequently asked questions

Q: What languages does the model support?
A: Currently, we mainly support English, other languages need to be trained with corresponding datasets.

Q: How can I improve the accuracy of my tests?
A: Performance can be improved by adding training data, tuning model parameters, and fine-tuning using domain-specific data.

Q: How can detection speed be optimized?
A: Detection speed can be improved by batch processing, model quantization, and using GPU acceleration.