# Copyright 2019 Baidu Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""Metric that tests models against contrast reductions."""
import numpy as np
from collections import Iterable
from tqdm import tqdm
from .base import Metric
from .base import call_decorator
[docs]class ContrastReductionMetric(Metric):
"""Metric that tests models against brightness variations."""
[docs] @call_decorator
def __call__(self, adv, annotation=None, unpack=True, abort_early=True,
threshold=1.0, epsilons=1000):
"""Reduces the contrast of the image until it is misclassified.
Parameters
----------
adv : `numpy.ndarray`
The original, unperturbed input as a `numpy.ndarray`.
annotation : int
The reference label of the original input.
unpack : bool
If true, returns the adversarial input, otherwise returns
the Adversarial object.
abort_early : bool
If true, returns when got first adversarial, otherwise
returns when all the iterations are finished.
threshold : float
Upper bound for contrast factor
epsilons : int or Iterable[float]
Either Iterable of contrast levels or number of contrast
levels between 1 and 0 that should be tried. Epsilons are
one minus the contrast level.
"""
a = adv
del adv
del annotation
del unpack
image = a.original_image
min_, max_ = a.bounds()
target = (max_ + min_) / 2
if not isinstance(epsilons, Iterable):
epsilons = np.linspace(0, threshold, num=epsilons + 1)[1:]
for epsilon in tqdm(epsilons):
perturbed = (1 - epsilon) * image + epsilon * target
_, is_adversarial = a.predictions(perturbed)
if is_adversarial and abort_early:
return