HiDDeN
HiDDeN copied to clipboard
Pytorch implementation of paper "HiDDeN: Hiding Data With Deep Networks" by Jiren Zhu, Russell Kaplan, Justin Johnson, and Li Fei-Fei
File "D:\AP\Anaconda\ANaconda\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "D:\AP\Anaconda\ANaconda\lib\site-packages\torch\nn\modules\loss.py", line 713, in forward return F.binary_cross_entropy_with_logits(input, target, File "D:\AP\Anaconda\ANaconda\lib\site-packages\torch\nn\functional.py", line 3132, in binary_cross_entropy_with_logits return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum)...
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [1, 1, 256, 256]], which is output 0 of UnsqueezeBackward1, is at version...
Hi, thanks for the code. I am currently trying to modify the embedded watermark into an image, but I have encountered many problems. Do you and your team have any...
Hi and thanks for the code and architecture! I was wondering why the results with the combined noise are lower than the ones from the paper. Indeed, the original paper...
Thanks for your contribution of this repository. I have a doubt. In the original paper, the authors mentioned that the experimental results given are all tested under real attacks, such...
I created a training set by sampling 10000 images from the Coco datasets. I have some questions: 1. I can reproduce the "crop" experiment's training footprint with a similar scale...
Can you kindly give me a detailed steps to run this project for a beginner guy like me. I can't figure out where to include my dataset folder(training and testing...
`--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[1], line 76 73 image = image.to(device) 74 message = torch.Tensor(np.random.choice([0, 1], (image.shape[0], hidden_config.message_length))).to(device) ---> 76 losses, (encoded_images, noised_images, decoded_messages) = model.validate_on_batch([image,...
If I want to adjust different attack intensity, for example, crop 0.5 to 0.8, do I need to retrain the model? If not, how do I adjust the attack intensity...
On line 183 of model/hidden.py: `def to_stirng(self):` should be `def to_string(self):`