Provable Secure Steganography Based on Adaptive Dynamic Sampling

# ADS Steganography This repo provides a minimal implementation of Adaptive Dynamic Sampling (ADS) steganography. You can run the pipeline via ADS.ipynb,including the encoding and decoding algorithms. ## Repository layout Repository structure ```text ADS-github/ ├── ADS.ipynb ├── README.md ├── requirements.txt ├── bit_stream.txt (secret messages, only for reference) ├── instinwild_en_4000.json (prompts) └── XHS.jsonl (prompts) ``` ## Installation Python 3.9+ recommended ```text pip install -r requirements.txt ``` ### Model In this experiment, we download large language models that are available on Hugging Face(https://huggingface.co/). Users also can load the model directly from Hugging Face using a model ID. ## Quick Start 1. Insstall dependencies. 2. Open the notebook: ADS.ipynb 3. Run cells in order to reproduce encoding/decoding. ## Secret message We use a randomly generated binary bitstream as the secret message in ADS.ipynb. The provided bit_stream.txt is only for reference; users may also choose to load bit_stream.txt as the secret message, which yields the same functionality. ## Datasets (prompts) • XHS.jsonl (Chinese prompts, JSON Lines) • instinwild_en_4000.json (English prompts, JSON) These are for quick checks; replace with your own prompts for experiments. ## Ethics Steganography is not a panacea that takes the risk of circumvention away. This repository is provided strictly for academic evaluation. Please comply with applicable laws, regulations, and platform policies.