Steganografiya quyidagi sohalarda qo'llaniladi, lekin ular bilan cheklanmaydi

# Continue while confidence < 1 -e
while S < (1 - error_rate) or threshold == 1:
old_threshold = threshold
threshold += precision
old_bound = floor(old_threshold * trigger_size) + 1
new_bound = floor(threshold * trigger_size) + 1
for i in range(old_bound, new_bound):
wrong_detected = (1 - 1 / number_labels)**(trigger_size - i)
S += comb(trigger_size, i) * \
(1 / number_labels**i) * wrong_detected

return min(threshold, 1)


def threshold_RMSE(upper_bound, lower_bound, q=3):
"""Compute threshold for RMSE metric.

Args:
upper_bound (int): Upper bound of regression outputs
lower_bound (int): lower bound of regression outputs
q (int): Quantification parameter

Returns:
threshold (float): The minimal threshold

"""
return (upper_bound - lower_bound) / q


def threshold_MAPE(upper_bound, lower_bound, q=3):
"""Compute threshold for MAPE metric.

Args:
upper_bound (int): Upper bound of regression outputs
lower_bound (int): lower bound of regression outputs
q (int): Quantification parameter

Returns:
threshold (float): The minimal threshold

"""
return (upper_bound - lower_bound) / (q * upper_bound)


def verify(outputs_original,
outputs_suspect,
number_labels=None,
error_rate=0.001,
bounds=None,
metric='accuracy',
dawn=False):
"""Verify watermark based on trigger outputs.

Args:
outputs_original (array): Predictions original
outputs_suspect (array): Predictions suspect