# Source code for perceptron.benchmarks.blended_noise

```
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Metric that tests models against blended uniform noise."""
import logging
import warnings
from collections import Iterable
import numpy as np
from tqdm import tqdm
from .base import Metric
from .base import call_decorator
from perceptron.utils.rngs import nprng
[docs]class BlendedUniformNoiseMetric(Metric):
"""Blends the image with a uniform noise image until it
is misclassified.
"""
[docs] @call_decorator
def __call__(self, adv, annotation=None, unpack=True,
abort_early=True, epsilons=10000, max_directions=1000):
"""Metric that tests models against blended uniform noise.
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.
epsilons : int or Iterable[float]
Either Iterable of standard deviations of the blended noise
or number of standard deviations between 0 and 1 that should
be tried.
max_directions : int
Maximum number of random images to try.
"""
a = adv
del adv
del annotation
del unpack
image = a.original_image
min_, max_ = a.bounds()
for j in tqdm(range(max_directions)):
# random noise images tend to be classified into the same class,
# so we might need to make very many draws if the original class
# is that one
random_image = nprng.uniform(
min_, max_, size=image.shape).astype(image.dtype)
_, is_adversarial = a.predictions(random_image)
if is_adversarial:
logging.info('Found adversarial image after {} '
'attempts'.format(j + 1))
break
else:
# never breaked
warnings.warn('BlendedUniformNoiseAttack failed to draw a'
' random image that is adversarial.')
if not isinstance(epsilons, Iterable):
epsilons = np.linspace(0, 1, num=epsilons + 1)[1:]
for epsilon in tqdm(epsilons):
perturbed = (1 - epsilon) * image + epsilon * random_image
# due to limited floating point precision,
# clipping can be required
if not a.in_bounds(perturbed): # pragma: no cover
np.clip(perturbed, min_, max_, out=perturbed)
_, is_adversarial = a.predictions(perturbed)
if is_adversarial and abort_early:
return
```