Source code for perceptron.benchmarks.salt_pepper

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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"""Metric that tests models against salt and pepper noise."""

import numpy as np
from tqdm import tqdm
from collections import Iterable
from .base import Metric
from .base import call_decorator
from perceptron.utils.rngs import nprng

[docs]class SaltAndPepperNoiseMetric(Metric): """Add salt and pepper noise."""
[docs] @call_decorator def __call__(self, adv, annotation=None, unpack=True, abort_early=True, epsilons=10000, repetitions=10): """Add salt and pepper 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 salt and pepper or number of standard deviations between 0 and 1 that should be tried. repetitions : int Specifies how often the attack will be repeated. """ a = adv del adv del annotation del unpack image = a.original_image min_, max_ = a.bounds() axis = a.channel_axis(batch=False) channels = image.shape[axis] shape = list(image.shape) shape[axis] = 1 r = max_ - min_ pixels = max_epsilon = 1 is_preset_eps = False if not isinstance(epsilons, Iterable): epsilon_n_steps = min(epsilons, pixels) else: is_preset_eps = True for _ in tqdm(range(repetitions)): if not is_preset_eps: epsilons = np.linspace( 0, max_epsilon, num=epsilon_n_steps + 1)[1:] for epsilon in epsilons: p = epsilon u = nprng.uniform(size=shape) u = u.repeat(channels, axis=axis) salt = (u >= 1 - p / 2).astype(image.dtype) * r pepper = -(u < p / 2).astype(image.dtype) * r perturbed = image + salt + pepper perturbed = np.clip(perturbed, min_, max_) if a.normalized_distance(perturbed) >= a.distance: continue _, is_adversarial = a.predictions(perturbed) if is_adversarial: # higher epsilon usually means larger perturbation, but # this relationship is not strictly monotonic, so we set # the new limit a bit higher than the best one so far if abort_early: return max_epsilon = epsilon * 1.2 break