Provided is code that demonstrates the training and evaluation of the work presented in the paper: "On the Detection of Digital Face Manipulation" published in CVPR 2020.

Hi, Thanks for releasing the code and dataset! Part of your dataset is generated by FaceApp (using automated scripts running on android devices). I am wondering if you could also release this android script? I also plan to generate some edited images using FaceApp, and an automated script will be quite helpful!! Thanks!

Thank you for releasing the code and the DFFD dataset!

I noticed that in the "faceapp" part of the dataset, there is a ground-truth manipulation masks image for each fake image. How are these mask images generated?

The paper mentioned that the ground-truth manipulation mask were calculated by source images and fake images, but I still did not understand how.

Thank you for answering my question. :)

Thanks for releasing the code and the dataset. I have some questions for the dataset,

• In align_faces/align_faces.m inside scripts.zip, there is a file called box.txt. But I can't find it anywhere. It seems crucial to align and crop the images.

• All of the images in dataset are in the resolution of 299x299. I wonder how did you process the images in CelebA. I remember the aligned and cropped image in CelebA are in the resolution of 128x128.

Hi, thanks for your work. I have a small question. The attention map size is 19x19, but the gt mask (diff image) is 299x299. Are they matched by downsampling gt mask?

Thanks for uploading your code and dataset. After a short view I'm considering your predicting process is like: generating masks with scripts on test data, using test data and their masks to feed into trained model to predict. But I was confused that in your test.py file, you get dataset like this:

def get_dataset():
  return Dataset('test', BATCH_SIZE, CONFIG['img_size'], CONFIG['map_size'], CONFIG['norms'], SEED)

then you differ masks of real and fake photos by using their labels in dataset.py:

  def __getitem__(self, index):
    im_name = self.images[index]
    img = self.load_image(im_name)
    if self.label_name == 'Real':
      msk = torch.zeros(1,19,19)
    else:
      msk = self.load_mask(im_name.replace('Fake/', 'Mask/'))
    return {'img': img, 'msk': msk, 'lab': self.label, 'im_name': im_name}

Is it fair to distinguish masks by label_name in the testing process? I also wonder how to create Mask/ folder when you predict fake images that donot have corresponding real images?

If i misunderstand anything please correct me, thanks a lot!

Hi,

For using pretrained model: xception-b5690688.pth, may I know where I can find the model specified here: https://github.com/JStehouwer/FFD_CVPR2020/blob/master/xception.py#L243

Thanks.

Greetings,

Many thanks to your wok. I am very interested in your work and I want to try out your model. When I ran the train*.py, I encounter the following issue , here are part of the error messages.

batch = [next(_.generator, None) for _ in self.datasets]

File "D:\Fake Detector\attention_map_to_detect_manipulation\FFD_CVPR2020\dataset.py", line 91, in self = reduction.pickle.load(from_parent)batch = [next(_.generator, None) for _ in self.datasets]

File "D:\Fake Detector\attention_map_to_detect_manipulation\FFD_CVPR2020\dataset.py", line 73, in get_batch EOFError: Ran out of input

and reduction.dump(process_obj, to_child) File "C:\Users\xxx\anaconda3\envs\d2l\lib\multiprocessing\reduction.py", line 60, in dump ForkingPickler(file, protocol).dump(obj) TypeError: cannot pickle 'generator' object

What I did is just make directory data/train/Real(Fake) and place my images dataset into the corresponding folder and then ran the train.py. However, it seems it can't work. May I ask whether I missed anything. I am running the program in windows system and I don't know that will affect as well.

Hi @JStehouwer - thank you so much for the awesome code (v2.1)!

I am trying to use your pretrained model on my own images in order to try out the classifier.

Are you able to confirm:

• Filename and format of pretrained model
• Whether anything else is needed to perform the above classification

Thanks again

1、 Whether the published dataset ( FFHQ、FaceAPP、StarGAN、PGGAN、StyleGAN ) has been randomly selected ? And How to generate starGAN mask, how to determine the specific CelebA picture used ? 2、 I have downloaded the FF++、CelebA and DeepFaceLab dataset, how to randomly select the training set, test set and verification set ? And how to set the random seed ? 3、 Which data sets need align processing, and how, please specify ?

Thank you for your work, it is very good, I will follow your work, but now the problem of dataset makes my work difficult, I hope to get your help.