Image Matching README

Introduction

Welcome to the Image Matching project! This repository contains a comprehensive implementation of two image matching algorithms. The Membership Scenario determines whether a query image possesses any matches among a database of images. The Index Scenario determines which specific database images, if any, match with a query image.

The primary goal of this project is to provide a robust framework for the comparison and matching of facial representation vectors using advanced cryptographic techniques, specifically homomorphic encryption.

Features

• Homomorphic Encryption: Leverages the OpenFHE library to perform secure computations on encrypted data.
• Biometric Standards: Complies with standards set by the National Institute of Standards and Technology (NIST).
• Parallel Processing: Supports parallel processing to enhance performance.

Requirements

• C++ Compiler: Ensure you have a modern C++ compiler that supports C++17 or later.
• CMake: Build system generator (version 3.10 or later).
• OpenFHE Library: For homomorphic encryption operations.
• Standard Libraries: Standard libraries for image processing and mathematical computations.

Installation

Step-by-Step Guide

1. Install Dependencies: Ensure you have all necessary dependencies installed:

   • OpenFHE
   • CMake
   • Standard C++ libraries

2. Clone the Repository:

   git clone https://github.com/n7koirala/image_matching.git
   cd image_matching

3. Build the Project:

   mkdir build
   cd build
   cmake ..
   make

4. Set up Experiment Tracking:

   mkdir output
   ../tools/setup_experiment.sh

Usage

Generating Experimental Datasets

To generate an experimental dataset, run the following script from the build folder:

../tools/generate_data.sh [FILENAME] [SIZE]

Note that the dataset will be automatically placed in the test folder. For example, to generate a dataset with 1024 database vectors located at /test/2^10.dat, try:

../tools/generate_data.sh "2^10.dat" $((2**10))

Running Experiments

To run the image matching application from the build folder, use the following command in your terminal:

./ImageMatching ../test/[FILENAME] [APPROACH]

The [FILENAME] parameter must correspond to an existing file generated by the

The [APPROACH] parameter determines which algorithm is used to perform the encrypted facial matching upon the provided dataset. The possibilities for this parameter are given below:

Parameter	Experimental Approach
1	Novel Stacked Linear Transform
2	Literature Baseline
3	GROTE (Baseline + Group Testing)
4	Blind-Match
5	Novel Diagonal Linear Transform

For instance, try:

./image_matching ../test/2^10.dat 1

This will execute the main application, showcasing both image matching algorithms, more specifically their encryption, matching, and decryption steps.

Configuration

Parameters

The application can be configured using various parameters defined in the source code. Key parameters include:

• Similarity Match Threshold: Set the cosine similarity value above which vectors are considered to be matching.
• Comparison Depth: Set the multiplicative depth to be used by the comparison-approximating function.
• Alpha-Norm Depth: Set the multiplicative depth to be used by the alpha-norm maximum approximation in the group-testing approach.
• CPU Cores: Set the maximum number of CPU cores to be used by the receiver or sender in multi-threaded operations.
• Security Level: Configure the security level of the CKKS scheme.
• Scaling Mod Size: Configure the size for the scaling modulus of the CKKS scheme.

Example Configuration

// include/config.h
const double MATCH_THRESHOLD = 0.85;
const size_t COMP_DEPTH = 10;
const size_t ALPHA_DEPTH = 2;
const size_t RECEIVER_NUM_CORES = 32;
const size_t SENDER_NUM_CORES = 32;

// src/main.cpp
CCParams<CryptoContextCKKSRNS> parameters;
parameters.SetSecurityLevel(HEStd_128_classic);
parameters.SetScalingModSize(45);
parameters.SetScalingTechnique(FIXEDMANUAL);

Contributing

We welcome contributions from the community to enhance the functionality and performance of the image matching project. Here’s how you can contribute:

1. Fork the Repository: Click on the fork button at the top right of the repository page.
2. Create a Branch: Create a new branch for your feature or bugfix.

   git checkout -b feature-name

3. Make Changes: Implement your changes in the new branch.
4. Submit a Pull Request: Push your changes to your forked repository and submit a pull request to the main repository.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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This README provides a comprehensive guide to understanding, installing, and contributing to the image matching project. For more detailed information, please refer to the source code and comments within the repository.