Steganografiya quyidagi sohalarda qo'llaniladi, lekin ular bilan cheklanmaydi

watermarked_dataset.append((x, y))

trainloader, valloader, testloader = self.loaders(watermarked_dataset)
triggerloader = torch.utils.data.DataLoader(
self.specialset, batch_size=self.args.trigger_size, shuffle=True)

return trainloader, valloader, testloader, triggerloader

def __write_letter(self, letter, data, offset):
"""Write text on input data through pyfliglet library.

Returns:
data (Object): data with inserted msg
"""
result = pyfiglet.figlet_format(letter, font="3x5")
result = result.replace('#', '1')
result = result.replace(' ', '0')
height = result.count('\n')
result = result.replace('\n', '0')
width = 4
x, y = offset

for i in range(height):
for j in range(width):
if int(result[i * (height - 1) + j]) == 1:
data[i + x][j + y] = 1

return data

def generate_trigger_patch_msg(self):
"""Generation trigger set with msg written
on the legitimate input

Returns:
trainloader (Object): training loader
valloader (Object): validation loader
testloader (Object): test loader
triggerloader (Object): trigger loader
"""
watermarked_dataset = []
for x, y in self.trainset:
watermarked_dataset.append((x, y))
# Sample the legitimate data to backdoor
sampled_triggerset = random.choices(watermarked_dataset, k=20)
triggerset = []
# Load parameters
msg = self.patch_args['msg']
target = self.patch_args['target']
for item_x, _ in sampled_triggerset:
k = 0
batch, size_x, size_y = item_x.shape