Refactor: Rename variable poisoned_persent to poisoned_percent

- Fixed typo in the variable name for improved clarity.
- Added descriptions to the attacks in the code for better understanding of functionality.

Author: FerTV

nebula/addons/attacks/poisoning/__init__.py

@@ -0,0 +1,11 @@
+"""
+Utilities for data poisoning techniques in machine learning. It includes three main components:
+
+1. **Data Poisoning (`datapoisoning.py`)**: Functions to apply various types of noise to training datasets, either randomly or targeted, altering the data to test the model's resilience against corrupted inputs.
+
+2. **Model Poisoning (`modelpoisoning.py`)**: A utility for injecting noise directly into a model's parameters, simulating attacks on model integrity by modifying the underlying weights and biases.
+
+3. **Label Flipping (`labelflipping.py`)**: Functions to randomly change labels within a dataset or target specific labels for modification, effectively simulating label corruption to evaluate the impact on model performance.
+
+Together, these components provide a comprehensive toolkit for researching and implementing poisoning attacks in machine learning systems, aiding in the development of more robust models.
+"""

nebula/addons/attacks/poisoning/datapoison.py

@@ -1,3 +1,15 @@
+"""
+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
 

nebula/addons/attacks/poisoning/labelflipping.py

@@ -1,3 +1,12 @@
+"""
+This module provides a function for label flipping in datasets, allowing for the simulation of label noise
+as a form of data poisoning. The main function modifies the labels of specific samples in a dataset based
+on a specified percentage and target conditions.
+
+Function:
+- labelFlipping: Flips the labels of a specified portion of a dataset to random values or to a specific target label.
+"""
+
 import copy
 import random
 

nebula/addons/attacks/poisoning/modelpoison.py

@@ -1,3 +1,14 @@
+"""
+This module provides a function for adding noise to a machine learning model's parameters, simulating
+data poisoning attacks. The main function allows for the injection of various types of noise into
+the model parameters, effectively altering them to test the model's robustness against malicious
+manipulations.
+
+Function:
+- modelpoison: Modifies the parameters of a model by injecting noise according to a specified ratio
+  and type of noise (e.g., Gaussian, salt, salt-and-pepper).
+"""
+
 from collections import OrderedDict
 
 import torch

nebula/core/datasets/changeablesubset.py

@@ -11,7 +11,7 @@ def __init__(
         indices,
         label_flipping=False,
         data_poisoning=False,
-        poisoned_persent=0,
+        poisoned_percent=0,
         poisoned_ratio=0,
         targeted=False,
         target_label=0,
@@ -24,7 +24,7 @@ def __init__(
         self.indices = indices
         self.label_flipping = label_flipping
         self.data_poisoning = data_poisoning
-        self.poisoned_persent = poisoned_persent
+        self.poisoned_percent = poisoned_percent
         self.poisoned_ratio = poisoned_ratio
         self.targeted = targeted
         self.target_label = target_label
@@ -35,7 +35,7 @@ def __init__(
             self.dataset = labelFlipping(
                 self.dataset,
                 self.indices,
-                self.poisoned_persent,
+                self.poisoned_percent,
                 self.targeted,
                 self.target_label,
                 self.target_changed_label,
@@ -44,7 +44,7 @@ def __init__(
             self.dataset = datapoison(
                 self.dataset,
                 self.indices,
-                self.poisoned_persent,
+                self.poisoned_percent,
                 self.poisoned_ratio,
                 self.targeted,
                 self.target_label,

nebula/core/datasets/datamodule.py

@@ -25,7 +25,7 @@ def __init__(
         val_percent=0.1,
         label_flipping=False,
         data_poisoning=False,
-        poisoned_persent=0,
+        poisoned_percent=0,
         poisoned_ratio=0,
         targeted=False,
         target_label=0,
@@ -46,7 +46,7 @@ def __init__(
         self.val_percent = val_percent
         self.label_flipping = label_flipping
         self.data_poisoning = data_poisoning
-        self.poisoned_persent = poisoned_persent
+        self.poisoned_percent = poisoned_percent
         self.poisoned_ratio = poisoned_ratio
         self.targeted = targeted
         self.target_label = target_label
@@ -70,7 +70,7 @@ def setup(self, stage=None):
                 self.train_set_indices,
                 label_flipping=self.label_flipping,
                 data_poisoning=self.data_poisoning,
-                poisoned_persent=self.poisoned_persent,
+                poisoned_percent=self.poisoned_percent,
                 poisoned_ratio=self.poisoned_ratio,
                 targeted=self.targeted,
                 target_label=self.target_label,

nebula/node.py

@@ -65,7 +65,7 @@ async def main(config):
     additional_node_round = config.participant["mobility_args"]["additional_node"]["round_start"]
 
     attacks = config.participant["adversarial_args"]["attacks"]
-    poisoned_persent = config.participant["adversarial_args"]["poisoned_sample_percent"]
+    poisoned_percent = config.participant["adversarial_args"]["poisoned_sample_percent"]
     poisoned_ratio = config.participant["adversarial_args"]["poisoned_ratio"]
     targeted = str(config.participant["adversarial_args"]["targeted"])
     target_label = config.participant["adversarial_args"]["target_label"]
@@ -96,7 +96,7 @@ async def main(config):
         label_flipping = False
         data_poisoning = False
         targeted = False
-        poisoned_persent = 0
+        poisoned_percent = 0
         poisoned_ratio = 0
 
     # Adjust the total number of nodes and the index of the current node for CFL, as it doesn't require a specific partition for the server (not used for training)
@@ -261,7 +261,7 @@ async def main(config):
         batch_size=dataset.batch_size,
         label_flipping=label_flipping,
         data_poisoning=data_poisoning,
-        poisoned_persent=poisoned_persent,
+        poisoned_percent=poisoned_percent,
         poisoned_ratio=poisoned_ratio,
         targeted=targeted,
         target_label=target_label,