Steganografiya quyidagi sohalarda qo'llaniladi, lekin ular bilan cheklanmaydi

acc_validation = round(100 * self.validate(), 4)
description = f'Validation accuracy: {acc_validation}'
pbar.set_description_str(description)

if self.args.encryption:
logger.info('Encryption of the triggers')
return self.encrypt()

if self.watermark:
return self.get_ownership()
else:
return None

def validate(self):
"""Validate the accuracy after each epoch.

Returns:
accuracy (float): accuracy on validation set

"""
correct = 0
with torch.no_grad():
self.model.eval().to(self.device)
for x, y in self.valloader:
probs = self.model(x.to(self.device))
predictions = torch.argmax(probs, 1).cpu().numpy()
correct += len(np.where(predictions == y.numpy())[0])
return correct / len(self.valset)

def test(self):
"""Test the accuracy of the data.

Returns:
accuracy (float): accuracy on test set

"""
correct = 0
with torch.no_grad():
self.model.eval().to(self.device)
for x, y in self.testloader:
probs = self.model(x.to(self.device))
predictions = torch.argmax(probs, 1).cpu().numpy()
correct += len(np.where(predictions == y.numpy())[0])
return correct / len(self.testset)

def verify(self, ownership, suspect=None):
"""Verify if a given model is stolen.

Args:
ownership (dict): ownership information
suspect (Object): the suspect model (None
if model not watermarked)

Returns:
is_stolen (bool): is the model stolen ?

"""

trigger_input = DataLoader(