Library for detecting image copy-move attack
Project description
This is a python package for detecting copy-move attack on a digital image.
More documentation is detailed on the Github page.
Example usage
API for the detection process
from pimage import copy_move
fraud_list, ground_truth_image, result_image = copy_move.detect("dataset_example_blur.png", block_size=32)
fraud_list
will be the list of(x_coordinate, y_coordinate)
and the number of the blocks. If the list is not empty, we can assume that the image is being tampered. For example:
means there are 5 possible matched/identical region with 2178 overlapping blocks on each of it((-57, -123), 2178) ((-11, 140), 2178) ((-280, 114), 2178) ((-34, -305), 2178) ((-37, 148), 2178)
ground_truth_image
contains the black and white ground truth of the detection resultresult_image
is the given image where the possible fraud region will be bordered (if any)
ground_truth_image
and result_image
will be formatted as numpy.ndarray
. It can further be processed. For example, it can be exported as image like so:
import imageio
imageio.imwrite("result_image.png", result_image)
imageio.imwrite("ground_truth_image.png", ground_truth_image)
Quick command to detect an image
To quickly run the detection command for your image, the copy_move.detect_and_export()
is also provided. The command is identical with .detect()
but it also save the result to desired output path.
from pimage import copy_move
copy_move.detect_and_export('dataset_example_blur.png', 'output', block_size=32)
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