SparSamp Artifact Description This repository contains the Artifact for the paper "SparSamp: Efficient Provably Secure Steganography Based on Sparse Sampling". SparSamp introduces a novel steganography scheme leveraging sparse sampling to achieve efficient and provably secure information hiding. Overview The Artifact provides a Python implementation for encoding and decoding messages using SparSamp within neural generative models. Core functionalities: Encoding: Embed messages into generated tokens via encode_spar. Decoding: Extract messages from tokens via decode_spar. Used Models & Datasets Models: Text: GPT-2 (openai-community/gpt2), Qwen-2.5 (Qwen/Qwen2.5-3B-Instruct), Llama-3 (meta-llama/Llama-3.1-8B-Instruct) Image: DDPM (FFHQ dataset) Audio: WaveRNN Datasets: IMDB text samples (first 3 sentences per sample). Key Features Provable Security Preserves original probability distributions (KLD = 0). High Efficiency (O(1)) time complexity per sampling step. Embedding speed up to 755 bits/s (GPT-2). Practicality Plug-and-play design: Replace sampling components in existing models. Supports multi-modal tasks (text, image, audio). Requirements Hardware CPU: Intel Xeon Gold 6330 @ 2.00GHz (minimum) GPU: NVIDIA RTX 4090 (recommended for acceleration) Memory: ≥128GB RAM Disk: ≥20GB for model checkpoints. Software Python: 3.8.5 Libraries: torch==2.2.2transformers==4.41.2scipy>=1.10