Repository files navigation

This project is part of our paper thathas been published at Springer. More detailed theories and steps are explained there.

To install the package, simply hit it with pip:pip3 install pimage. Example script for using this package is also providedhere.

The algorithm can be dynamically configured withConfiguration class. If omitted, the default value from both of the paper will be used.The default value and description for each of the parameter is detailed onconfiguration.py.

frompimage.configurationimportConfigurationconf=Configuration(block_size=32,nn=2,nf=188,nd=50,p=(1.80,1.80,1.80,0.0125,0.0125,0.0125,0.0125),t1=2.80,t2=0.02)

• Determining theblock_size: The first algorithm use block size of32 pixels so this package will use the same value by default. Increasing the size means faster run time at a reduced accuracy. Analogically, decreasing the size means longer run time with increased accuracy.

• The API for detection process is provided viacopy_move.detect() method. For example:

  frompimageimportcopy_movefrompimage.configurationimportConfigurationconf=Configuration(block_size=32)fraud_list,ground_truth_image,result_image=copy_move.detect("dataset_example_blur.png",configuration=conf)

• fraud_list will be the list of(x_coordinate, y_coordinate) of the blocks group and the total number of the blocks it is formed with. If this list is not empty, we can assume that the image is being tampered. For example, running thecattle dataset with 32 px of block size will result in:

  ((-57, -123), 2178)((-11, 140), 2178)((-280, 114), 2178)((-34, -305), 2178)((-37, 148), 2178)

  the above output means there are 5 possible matched/identical region with 2178 overlapping blocks on each of it

• ground_truth_image contains the black and white ground truth of the detection result. This is useful for comparing accuracy, MSE, etc with the ground truth from the dataset

• result_image is the given image where the possible fraud region will be color-bordered (if any)

ground_truth_image andresult_image will be formatted asnumpy.ndarray. It can further be processed as needed. For example, it can be programmatically modified and then exported later as image like so:

importimageioimageio.imwrite("result_image.png",result_image)imageio.imwrite("ground_truth_image.png",ground_truth_image)

To quickly run the detection command for your image, thecopy_move.detect_and_export() is also provided. The command is identical with.detect() but it also save the result to desired output path.

frompimageimportcopy_movecopy_move.detect_and_export('dataset_example_blur.png','output')

this code will save theground_truth_image andresult_image insideoutput folder.

When runningcopy_move.detect() orcopy_move.detect_and_export(), you can passverbose=True to outputthe status of each step. The default value will beFalse so nothing will be printed.

Example output when verbose mode is being enabled:

Processing: dataset/multi_paste/cattle_gcs500_copy_rb5.pngStep 1 of 4: Object and variable initializationStep 2 of 4: Computing characteristic features100%|██████████| 609/609 [04:14<00:00,  2.39it/s]Step 3 of 4:Pairing image blocks100%|██████████| 241163/241163 [00:00<00:00, 816659.95it/s]Step 4 of 4: Image reconstructionFound pair(s) of possible fraud attack:((-57, -123), 2178)((-11, 140), 2178)((-280, 114), 2178)((-34, -305), 2178)((-37, 148), 2178)Computing time : 254.81 secondSorting time   : 0.89 secondAnalyzing time : 0.3 secondImage creation : 1.4 secondTotal time    : 0:04:17 second

The implementation generally manipulates overlapping blocks, and are constructed based on two algorithms:

1. Duplication detection algorithm, taken fromExposing Digital Forgeries by Detecting Duplicated Image Region (alternative link); Fast and smooth attack detection algorithm on digital image usingprincipal component analysis, but sensitive to noise and any following manipulations that are being applied after the attack phase (in which they call itpost region duplication process)
2. Robust detection algorithm, taken fromRobust Detection of Region-Duplication Forgery in Digital Image; Relatively slower process with rough result on the detection edge but are considered robust towards noise andpost region duplication process

We know that the first algorithm usecoordinate andprincipal_component features, while the second algorithm usecoordinate andseven_features.

Knowing that, we then attempt to give a tolerance by merging all the features like so:

The attributes are saved as one object. A lexicographical sorting is then applied to the principal component and the seven features.

The principal component will bring similar block closer, while the seven features will back up the detection for a block that can't be detected by principal component due to being applied with post region duplication process (for example being blurred).

By doing so, the new algorithm will have a tolerance regarding variety of the input image. The detection result will be relatively smooth and accurate for any type of image, with a trade-off in run time as we basically run two algorithm.

All the result of the dataset should be insideoutput directory of this repository.

The image shown is ordered as: original, attacked, and the resulting detection image.

The project is formerly written with Python 2 for our Undergraduate Thesis, which is now left unmaintainedhere. The original thesis is written in Indonesian that in any case can also be downloaded fromhere.