mayfly227 / isonet-1 Goto Github PK

The ISO setting, which is also known as film speed, determines the noise characteristics of the output images. As a consequence, it plays an important role in noise based forensics. However, the ISO setting information from the image metadata may be unavailable in practice. Hence, estimating the ISO setting of a probe image from its content is of forensic significance. In this work, we propose a convolutional neural network, which is called ISONet, for ISO setting estimation purpose. The proposed ISONet can successfully infer the ISO setting both globally (image-level) and locally (patch-level). It is not only work on uncompressed images, but also effective on JPEG compressed images. We apply the ISONet into two typical forensic scenarios, one is the image splicing localization and the other is the Photo Response Non-Uniformity (PRNU) correlation prediction. A series of experiments show that the ISONet can yield a remarkable improvement in both forensic scenarios.

submitted to IWDW2020

From running our demo,following packages are required

• pytorch >=1.3
• opencv-python
• matplotlib
• h5py
• numpy
• tensorboard

Run python test_model.py --model_path models/net_tif.pth --pic_path image_path
or
Run Fig3.py to repeat the ISO metric map used for Fig.3

Runpython test_model.py --model_path models/net_jpg.pth --pic_path image_path
or
Run Fig5.py to repeat the global ISO metric used for Fig.5

Take ISONet_Uncompressed for example

1. Download the training images to IMG_DATA_DIR of your computer

2. runprepare_data.py to generate the training set

   python prepare_data.py --aug_times 3 --pic_type tif --data_path IMG_DATA_DIR --save_path DATA_PATH

3. Run main_train.py to train the model python main_train.py --data_path DATA_PATH

Most of the codes are provided by Kang Deng, [email protected]