对于AUC,PSCC-Net在所有情况下均优于基准(平均超过SPAN 2.4%)。对于F1分数,我们的模型大幅优于它们(平均超过SPAN 16.6%)。这证实了我们整体网络设计的有效性。 图5. 在四个标准测试数据集上的定位性能定性评估结果。从上到下,我们的PSCC-Net与SOTA方法在Columbia、Coverage、CASIA和NIST16数据集上进行比较,每个...
PSCC-Net processes the image in a two-path procedure: a top-down path that extracts local and global features and a bottom-up path that detects whether the input image is manipulated, and estimates its manipulation masks at multiple scales, where each mask is conditioned on the previous one...
To defend against manipulation of image content, such as splicing, copy-move, and removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to detect and localize image manipulations. PSCC-Net processes the image in a two-path procedure: a top-down path that extracts loc...
Subsequently, run train.py to retrain the PSCC-Net.All hyper-parameters about training are stored in utils/config.py. You may modify them to accommodate your system. Besides, the weights of PSCC-Net are stored in the checkpoint folder....
Since there is no standard IMDL dataset for training, a synthetic dataset is built to train and validate our PSCC-Net. This dataset includes four classes. More details are shown as follows. The codes for dataset generation are provided in ```dataset_generation/```, and the download link ...
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帮助菜单，更新。试一下。
帮助菜单，更新。试一下。
To test the PSCC-Net, simply runtest.py. It will probe the images in thesamplefolder that contains 6 authentic images and 6 forged images including splicing, copy-move, and removal manipulations. You can also put other images in this folder for testing. ...