Steganografiya quyidagi sohalarda qo'llaniladi, lekin ular bilan cheklanmaydi

if self.criterion == 'cross-entropy':
self.criterion = nn.CrossEntropyLoss()


import torchvision
from torchvision import transforms


def load_trigger(path, shape):
"""Load trigger data.

Returns:
path (str): trigger images folder
shape (tuple): tuple for dimension of the data

"""
x, y, z = shape
modification = [transforms.Resize((y, z)), transforms.CenterCrop(y)]
# Convert 3 channels to 1
if x == 1:
modification.append(transforms.Grayscale(num_output_channels=1))
modification.append(transforms.ToTensor())
else:
modification.append(transforms.ToTensor())
transformation = torchvision.transforms.Compose(modification)
specialset = torchvision.datasets.ImageFolder(path,
transform=transformation)
return specialset


from math import floor, sqrt

from scipy.special import comb


def threshold_classifier(trigger_size, number_labels, error_rate=0.001):
"""Compute threshold for classification task.

Args:
trigger_size (int): Number of trigger instances
number_labels (int): Number of labels
error_rate (int): Error rate of verification

Returns:
threshold (float): The minimal threshold

"""
# Define parameters
threshold = 1 / number_labels
precision = 1 / trigger_size
S = 0

# Compute confidence level

for i in range(0, int(threshold * trigger_size) + 1):
wrong_detected = ((1 - 1 / number_labels)**(trigger_size - i))
S += comb(trigger_size, i) * (1 / (number_labels**i)) * wrong_detected