有关定量比较和为了节省运行时间提前终止 PSCC-Net 的更多讨论可以在第 IV-F 节中找到。 F. 运行时分析和消融研究 在表V中,我们测试了几个PSCC-Net的变体,以验证网络设计,其中所有变体都在我们的合成数据集上进行了预训练。平均AUC/F1值以百分比报告,并且运行时(比例)相对于PSCC-Net报告。我们的完整模型需要...
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. ...