lidar-steganography's Introduction

LiDAR Steganography through prompt directed segmentation

Project Overview

This project introduces a novel method for embedding secret messages into specific areas of point cloud datasets, identified through user-defined prompts.

Key Features and Workflow:

Prompt-Directed Area Selection: Utilizing the LangSAM model, we allow users to select specific regions within a LiDAR scan using descriptive text prompts.
Efficient Message Encoding: Once the region is selected, the message is encoded using the Least Significant Bit (LSB) technique into the RGB channels of the point cloud data. This method balances data integrity and concealment, making the process efficient and discreet.
Selective Decoding: The recipient of the encoded LiDAR data can use the same prompt to segment the same scan area. This facilitates precise and efficient decoding of the hidden message from the specified region, avoiding the need to process the entire scan.

To set up your Conda environment using the environment.yml file, follow these steps:

conda env create -f environment.yml
conda activate lidar-steg

Accessing LiDAR Scans

The LiDAR scan data required for this project is stored in a Google Drive folder. You can access this data by following the link below:

Access LiDAR Scan Data

Interactive Google Colab Demo

You can run and experiment with the scan decoding experiment in colab, without any local setup. To access and use the notebook, follow the link below:

Open Google Colab Demo

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