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Image Manipulation Localization (IML) task involves detecting and locating tampered regions within images, which can be regarded as a countermeasure against methods like Photoshop or Deepfake.
However, there are many issues or resolution misalignment in the existing datasets, meaning the manipulated images and their corresponding masks do not have the same resolution. This is problematic, so this repository has addressed this issue present in various datasets.
For instance, here are some examples of problematic images in the CASIAv2 dataset:
Thus, we collected the images with this issue and revised the corresponding mask.
We also point out some minor errors here, and welcome anyone to raise issues or submit pull requests to share various problems existing in the IML dataset, so that the community can work together to solve them.
- Issue: There is an extra image (
CASIA1.0/Modified Tp/Tp/Sp_D_NRN_A_cha0011_sec0011_0542.jpg) without a mask in the CASIAv1 dataset - Solution: We recommend removing it during training or evaluation.
- Issue: There are 17 images with resolution misalignment problems.
- File name of these images and the resolution of images & masks:
[["Tp_D_CNN_M_N_sec00011_cha00085_11227.jpg", [256, 384, 3], [384, 256, 3]], ["Tp_D_CRN_S_N_ani10191_ani10190_12437.jpg", [638, 336, 3], [336, 638, 3]], ["Tp_D_CRN_S_N_nat10130_pla00049_11524.jpg", [256, 384, 3], [384, 256, 3]], ["Tp_D_NND_M_B_nat20098_nat20073_01602.tif", [387, 581, 3], [382, 581, 3]], ["Tp_D_NRN_M_N_nat10134_nat00095_11912.jpg", [600, 600, 3], [475, 600, 3]], ["Tp_D_NRN_M_N_nat10134_nat10124_11913.jpg", [600, 600, 3], [475, 600, 3]], ["Tp_S_CRN_S_N_art00059_art00059_10508.tif", [256, 384, 3], [384, 256, 3]], ["Tp_S_NND_S_N_sec20064_sec20064_01654.tif", [647, 416, 3], [636, 416, 3]], ["Tp_S_NNN_S_N_art20077_art20077_01883.tif", [867, 578, 3], [864, 573, 3]], ["Tp_S_NNN_S_N_ind20037_ind20037_01778.tif", [578, 863, 3], [569, 862, 3]], ["Tp_S_NNN_S_N_sec00012_sec00012_11230.jpg", [256, 384, 3], [384, 256, 3]], ["Tp_S_NNN_S_N_sec00074_sec00074_00751.tif", [384, 256, 3], [384, 255, 3]], ["Tp_S_NRD_S_N_arc20079_arc20079_01719.tif", [392, 591, 3], [383, 582, 3]], ["Tp_S_NRD_S_N_pla20071_pla20071_01971.tif", [501, 760, 3], [499, 760, 3]], ["Tp_S_NRN_S_B_ind10002_ind10002_20010.jpg", [600, 450, 3], [800, 600, 3]], ["Tp_S_NRN_S_N_art20077_art20077_02316.tif", [863, 574, 3], [863, 572, 3]], ["Tp_S_NRN_S_N_pla20080_pla20080_01980.tif", [781, 514, 3], [781, 512, 3]]]
- File name of these images and the resolution of images & masks:
- Solution: We fixed them and released the download link for these images.
- For only revised images, download from Google Drive.
- For only revised images, download from Baidu Netdisk.
- For the full revised dataset, go to this repo.
- Issue: There are 9 images (27 masks) with resolution misalignment problems.
41copy.tif 41forged.tif 41paste.tif 48copy.tif 48forged.tif 48paste.tif 55copy.tif 55forged.tif 55paste.tif 56copy.tif 56forged.tif 56paste.tif 57copy.tif 57forged.tif 57paste.tif 58copy.tif 58forged.tif 58paste.tif 59copy.tif 59forged.tif 59paste.tif 61copy.tif 61forged.tif 61paste.tif 95copy.tif 95forged.tif 95paste.tif - Solution: We fixed them and released the download link for these images.
- For only revised images, download from Google Drive.
- For only revised images, download from Baidu Netdisk.
- Issue: There is a single image(
IMD2020/z14/00030_fake.jpg) with a resolution misalignment problem. - Solution: We simply upload the revised mask here, you can just download it directly:
- If you come across any other issues in datasets within the IML field, feel free to point out them in the Discussions and let the open-source community work together to resolve them.
- If you find our work valuable, please consider giving us a star⭐️ and sharing it with others. Your support helps us gain more recognition and encourages further collaboration within the community.