refactor: reorganize attacks folder structure

nebula/addons/attacks/communications/delayerattack.py

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+import asyncio
+from functools import wraps
+import logging
+import types
+from nebula.addons.attacks.attacks import Attack
+
+
+class DelayerAttack(Attack):
+    """
+    Implements an attack that delays the execution of a target method by a specified amount of time.
+
+    This attack dynamically modifies the `propagate` method of the propagator to
+    introduce a delay during its execution.
+    """
+    def __init__(self, engine, attack_params):
+        """
+        Initializes the DelayerAttack with the engine and attack parameters.
+
+        Args:
+            engine: The engine managing the attack context.
+            attack_params (dict): Parameters for the attack, including the delay duration.
+        """
+        super().__init__()
+        self.engine = engine
+        self.propagator = self.engine._cm._propagator
+        self.original_propagate = self.propagator.propagate
+        self.delay = int(attack_params["delay"])
+        self.round_start_attack = int(attack_params["round_start_attack"])
+        self.round_stop_attack = int(attack_params["round_stop_attack"])
+
+    def delay_decorator(self, delay):
+        """
+        Decorator that adds a delay to the execution of the original method.
+
+        Args:
+            delay (int or float): The time in seconds to delay the method execution.
+
+        Returns:
+            function: A decorator function that wraps the target method with the delay logic.
+        """
+        # The actual decorator function that will be applied to the target method
+        def decorator(func):
+            @wraps(func)  # Preserves the metadata of the original function
+            async def wrapper(*args):
+                logging.info(f"[DelayerAttack] Adding delay of {delay} seconds")
+
+                await asyncio.sleep(delay)
+                _, *new_args = args  # Exclude self argument
+                return await func(*new_args)
+            return wrapper
+        return decorator
+
+    async def _inject_malicious_behaviour(self):
+        """
+        Modifies the `propagate` method of the propagator to include a delay.
+        """
+        decorated_propagate = self.delay_decorator(self.delay)(self.propagator.propagate)
+
+        self.propagator.propagate = types.MethodType(decorated_propagate, self.propagator)
+
+    async def _restore_original_behaviour(self):
+        """
+        Restores the original behaviour of the `propagate` method.
+        """
+        self.propagator.propagate = self.original_propagate
+
+    async def attack(self):
+        """
+        Starts the attack by injecting the malicious behaviour.
+
+        If the current round matches the attack start round, the malicious behavior
+        is injected. If it matches the stop round, the original behavior is restored.
+        """
+        if self.engine.round == self.round_stop_attack:
+            logging.info(f"[DelayerAttack] Stopping Delayer attack")
+            await self._restore_original_behaviour()
+        elif self.engine.round == self.round_start_attack:
+            logging.info("[DelayerAttack] Injecting malicious behaviour")
+            await self._inject_malicious_behaviour()

nebula/addons/attacks/dataset/datapoison.py

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+"""
+This module contains functions for applying data poisoning techniques,
+including the application of noise to tensors and modification of datasets
+to simulate poisoning attacks.
+
+Functions:
+- apply_noise: Applies noise to a tensor based on the specified noise type and poisoning ratio.
+- datapoison: Adds noise to a specified portion of a dataset for data poisoning purposes.
+- add_x_to_image: Adds an 'X' mark to the top-left corner of an image.
+- poison_to_nlp_rawdata: Poisons NLP data by setting word vectors to zero with a given probability.
+"""
+
+import copy
+import random
+
+import numpy as np
+import torch
+from skimage.util import random_noise
+
+from nebula.addons.attacks.dataset.datasetattack import DatasetAttack
+
+class SamplePoisoningAttack(DatasetAttack):
+    """
+    Implements a data poisoning attack on a training dataset.
+
+    This attack introduces noise or modifies specific data points to influence 
+    the behavior of a machine learning model.
+
+    Args:
+        engine (object): The training engine object, including the associated 
+                         datamodule.
+        attack_params (dict): Attack parameters including:
+            - poisoned_percent (float): The percentage of data points to be poisoned.
+            - poisoned_ratio (float): The ratio of poisoned data relative to the total dataset.
+            - targeted (bool): Whether the attack is targeted at a specific label.
+            - target_label (int): The target label for the attack (used if targeted is True).
+            - noise_type (str): The type of noise to introduce during the attack.
+    """
+    def __init__(self, engine, attack_params):
+        """
+        Initializes the SamplePoisoningAttack with the specified engine and parameters.
+
+        Args:
+            engine (object): The training engine object.
+            attack_params (dict): Dictionary of attack parameters.
+        """
+        super().__init__(engine)
+        self.datamodule = engine._trainer.datamodule
+        self.poisoned_percent = float(attack_params["poisoned_percent"])
+        self.poisoned_ratio = float(attack_params["poisoned_ratio"])
+        self.targeted = attack_params["targeted"]
+        self.target_label = int(attack_params["target_label"])
+        self.noise_type = attack_params["noise_type"]
+        self.round_start_attack = int(attack_params["round_start_attack"])
+        self.round_stop_attack = int(attack_params["round_stop_attack"])
+
+
+    def apply_noise(self, t, noise_type, poisoned_ratio):
+        """
+        Applies noise to a tensor based on the specified noise type and poisoning ratio.
+
+        Args:
+            t (torch.Tensor): The input tensor to which noise will be applied.
+            noise_type (str): The type of noise to apply. Supported types are:
+                - "salt": Salt noise (binary salt-and-pepper noise with only 'salt').
+                - "gaussian": Gaussian noise with mean 0 and specified variance.
+                - "s&p": Salt-and-pepper noise.
+                - "nlp_rawdata": Applies a custom NLP raw data poisoning function.
+            poisoned_ratio (float): The ratio or variance of noise to be applied, depending on the noise type.
+
+        Returns:
+            torch.Tensor: The tensor with noise applied. If the noise type is not supported,
+                          returns the original tensor with an error message printed.
+
+        Raises:
+            ValueError: If the specified noise_type is not supported.
+
+        Notes:
+           - The "nlp_rawdata" noise type requires the custom `poison_to_nlp_rawdata` function.
+           - Noise for types "salt", "gaussian", and "s&p" is generated using `random_noise` from
+             the `skimage.util` package, and returned as a `torch.Tensor`.
+        """
+        if noise_type == "salt":
+            return torch.tensor(random_noise(t, mode=noise_type, amount=poisoned_ratio))
+        elif noise_type == "gaussian":
+            return torch.tensor(random_noise(t, mode=noise_type, mean=0, var=poisoned_ratio, clip=True))
+        elif noise_type == "s&p":
+            return torch.tensor(random_noise(t, mode=noise_type, amount=poisoned_ratio))
+        elif noise_type == "nlp_rawdata":
+            return self.poison_to_nlp_rawdata(t, poisoned_ratio)
+        else:
+            print("ERROR: poison attack type not supported.")
+            return t
+
+
+    def datapoison(
+    self,
+    dataset,
+    indices,
+    poisoned_percent,
+    poisoned_ratio,
+    targeted=False,
+    target_label=3,
+    noise_type="salt",
+    ):
+        """
+        Adds noise to a specified portion of a dataset for data poisoning purposes.
+
+        This function applies noise to randomly selected samples within a dataset.
+        Noise can be targeted or non-targeted. In non-targeted poisoning, random samples
+        are chosen and altered using the specified noise type and ratio. In targeted poisoning,
+        only samples with a specified label are altered by adding an 'X' pattern.
+
+        Args:
+            dataset (Dataset): The dataset to poison, expected to have `.data` and `.targets` attributes.
+            indices (list of int): The list of indices in the dataset to consider for poisoning.
+            poisoned_percent (float): The percentage of `indices` to poison, as a fraction (0 <= poisoned_percent <= 1).
+            poisoned_ratio (float): The intensity or probability parameter for the noise, depending on the noise type.
+            targeted (bool, optional): If True, applies targeted poisoning by adding an 'X' only to samples with `target_label`.
+                                       Default is False.
+            target_label (int, optional): The label to target when `targeted` is True. Default is 3.
+            noise_type (str, optional): The type of noise to apply in non-targeted poisoning. Supported types are:
+                                        - "salt": Applies salt noise.
+                                        - "gaussian": Applies Gaussian noise.
+                                        - "s&p": Applies salt-and-pepper noise.
+                                        Default is "salt".
+
+        Returns:
+            Dataset: A deep copy of the original dataset with poisoned data in `.data`.
+
+        Raises:
+            ValueError: If `poisoned_percent` is not between 0 and 1, or if `noise_type` is unsupported.
+
+        Notes:
+            - Non-targeted poisoning randomly selects samples from `indices` based on `poisoned_percent`.
+            - Targeted poisoning modifies only samples with `target_label` by adding an 'X' pattern, regardless of `poisoned_ratio`.
+        """
+        new_dataset = copy.deepcopy(dataset)
+        train_data = new_dataset.data
+        targets = new_dataset.targets
+        num_indices = len(indices)
+        if not isinstance(noise_type, str):
+            noise_type = noise_type[0]
+
+        if not targeted:
+            num_poisoned = int(poisoned_percent * num_indices)
+            if num_indices == 0:
+                return new_dataset
+            if num_poisoned > num_indices:
+                return new_dataset
+            poisoned_indice = random.sample(indices, num_poisoned)
+
+            for i in poisoned_indice:
+                t = train_data[i]
+                poisoned =  self.apply_noise(t, noise_type, poisoned_ratio)
+                train_data[i] = poisoned
+        else:
+            for i in indices:
+                if int(targets[i]) == int(target_label):
+                    t = train_data[i]
+                    poisoned = self.add_x_to_image(t)
+                    train_data[i] = poisoned
+        new_dataset.data = train_data
+        return new_dataset
+
+
+    def add_x_to_image(self, img):
+        """
+        Adds a 10x10 pixel 'X' mark to the top-left corner of an image.
+
+        This function modifies the input image by setting specific pixels in the
+        top-left 10x10 region to a high intensity value, forming an 'X' shape.
+        Pixels on or below the main diagonal and above the secondary diagonal
+        are set to 255 (white).
+
+        Args:
+            img (array-like): A 2D array or image tensor representing pixel values.
+                              It is expected to be in grayscale, where each pixel
+                              has a single intensity value.
+
+        Returns:
+            torch.Tensor: A tensor representation of the modified image with the 'X' mark.
+        """
+        for i in range(0, 10):
+            for j in range(0, 10):
+                if i + j <= 9 or i == j:
+                    img[i][j] = 255
+        return torch.tensor(img)
+
+
+    def poison_to_nlp_rawdata(self, text_data, poisoned_ratio):
+        """
+        Poisons NLP data by setting word vectors to zero with a given probability.
+
+        This function randomly selects a portion of non-zero word vectors in the
+        input text data and sets them to zero vectors based on the specified
+        poisoning ratio. This simulates a form of data corruption by partially
+        nullifying the information in the input data.
+
+        Args:
+            text_data (list of torch.Tensor): A list where each entry is a tensor
+                representing a word vector. Non-zero vectors are assumed to represent valid words.
+            poisoned_ratio (float): The fraction of non-zero word vectors to set to zero,
+                where 0 <= poisoned_ratio <= 1.
+
+        Returns:
+            list of torch.Tensor: The modified text data with some word vectors set to zero.
+
+        Raises:
+            ValueError: If `poisoned_ratio` is greater than 1 or less than 0.
+
+        Notes:
+            - `poisoned_ratio` controls the percentage of non-zero vectors to poison.
+            - If `num_poisoned_token` is zero or exceeds the number of non-zero vectors,
+              the function returns the original `text_data` without modification.
+        """
+        non_zero_vector_indice = [i for i in range(0, len(text_data)) if text_data[i][0] != 0]
+        non_zero_vector_len = len(non_zero_vector_indice)
+
+        num_poisoned_token = int(poisoned_ratio * non_zero_vector_len)
+        if num_poisoned_token == 0:
+            return text_data
+        if num_poisoned_token > non_zero_vector_len:
+            return text_data
+
+        poisoned_token_indice = random.sample(non_zero_vector_indice, num_poisoned_token)
+        zero_vector = torch.Tensor(np.zeros(len(text_data[0][0])))
+        for i in poisoned_token_indice:
+            text_data[i] = zero_vector
+        return text_data
+
+
+    def get_malicious_dataset(self):
+        """
+        Generates a poisoned dataset based on the specified parameters.
+
+        Returns:
+            Dataset: A modified version of the training dataset with poisoned data.
+        """
+        return self.datapoison(
+            self.datamodule.train_set,
+            self.datamodule.train_set_indices,
+            self.poisoned_percent,
+            self.poisoned_ratio,
+            self.targeted,
+            self.target_label,
+            self.noise_type
+        )
+