CMFD

Copy-Move Forgery Detection and Localization This is a course project for media information security

Introduction

Copy-Move Forgery Detection (CMFD) is a technique to detect and localize copy-move forgery in images. The goal of this project is to implement multiple CMFD algorithms in python and evaluate the performance.

We design a framework to evaluate the performance of the algorithm. The framework is based on PyTorch and can be easily extended to other algorithms.

Besides, we also implement a baseline algorithm (SIFT) and enhance it with patched self-adaptive methods to improve the performance.

Group Info

• Haotian Hong
• Zhenyu Jin

Phenomenon: Talk is cheap, show me the code.

Dataset

MICC-F220: this dataset is composed by 220 images; 110 are tampered and 110 originals.

Pre-requisites

• python>=3.7
• opencv-python
• numpy
• sklearn
• torch
• pandas

Installation

git clone https://github.com/bughht/CMFD.git
cd CMFD
wget http://lci.micc.unifi.it/labd/cmfd/MICC-F220.zip
unzip MICC-F220.zip
pip install -r Requirements.txt

Usage

Run the baseline

python AlgoTest.py -a SIFT_Methods

Design your own algorithm

Make sure your algorithm is written in the format below:

Filename: MyAlgorithm.py

class MyAlgorithm:
    def __init__(self, **kwargs):
        # initialize your algorithm

    def predict(self, img):
        # detect copy-move forgery in the image
        # return the classification result (0 or 1)

Then run the following command:

python AlgoTest.py -a MyAlgorithm

or

python AlgoTest.py --algorithm MyAlgorithm

Baseline: SIFT

The baseline is a sift-based algorithm implemented in Python. With current parameters, the evaluation of this algorithm on MICC-F220 is shown below.

Accuracy: 81.36% Precision: 76.74% Recall: 90.00% F1 Score: 82.85%

Enhancement: Patch-SIFT

• Principle: Split images into patches and adapt the parameters of SIFT (sigma) to the smoothness of the patch.
• Algorithm:
  • Split the image into patches
  • For each patch, calculate the smoothness of the patch (using the variance of the Laplacian)
  • For each patch, adapt the parameters of SIFT (sigma) to the smoothness of the patch (using linear model) and apply SIFT to the patch
  • Apply Brute-Force Matching to the image
  • Evaluate the performance of the algorithm

Accuracy: 86.82% Precision: 86.49% Recall: 87.27% F1 Score: 86.88%

Experiment Results

We've tested the following algorithms on MICC-F220 dataset based on our framework:

• Patch-SIFT
• SIFT
• ORB
• FAST

Patch-SIFT

Patch-SIFT	precision	recall	f1-score	support
No Copy-Move	0.87	0.86	0.87	110
Copy-Move	0.86	0.87	0.87	110

• Accuracy:86.82% Precision:86.49% Recall:87.27%
• Confusion Matrix

SIFT

SIFT	precision	recall	f1-score	support
No Copy-Move	0.88	0.73	0.80	110
Copy-Move	0.77	0.90	0.83	110

• Accuracy:81.36% Precision:76.74% Recall:90.00% F1 Score:82.85%
• Confusion-Matrix:

ORB

ORB	precision	recall	f1-score	support
No Copy-Move	0.65	0.75	0.69	110
Copy-Move	0.70	0.60	0.65	110

• Accuracy:67.27% Precision:70.21% Recall:60.00% F1 Score:64.71%
• Confusion-Matrix:

Project Goals Checkbox

• Implement feature-point-based algorithms
  • Key Points Extraction
    • SIFT
    • ORB
    • FAST
    • Harris Corner
  • Feature Descriptor
    • SIFT feature
    • ORB feature
• Implement matching algorithms
  • Brute-force matching
  • Fann matching
• Design a model performance evaluation framework
  • Torch Dataset and DataLoader wrapper
  • Model performance evaluation
• Enhance one of the algorithm tested above

Contribution

We are welcome to any contribution to this project. If you are interested in this project, please contact us.

References

Expand all

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