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edeca31 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 | """Base class for Membership Inference Attacks."""
from abc import ABC, abstractmethod
from typing import Dict, Optional, Literal
import numpy as np
from mia.experiment import MIAExperiment
from mia.predictions import PredictionCache
AccessLevel = Literal["blackbox", "greybox", "whitebox"]
class BaseAttack(ABC):
"""
Abstract base class for MIA attacks.
Defines the interface for all attack implementations and provides
common functionality for loading data.
"""
def __init__(
self,
experiment: MIAExperiment,
prediction_cache: PredictionCache,
access_level: AccessLevel = "blackbox",
):
"""
Initialize attack.
Args:
experiment: MIA experiment with membership matrices
prediction_cache: Cache with model predictions
access_level: Access level for the attack
"""
self.experiment = experiment
self.prediction_cache = prediction_cache
self.access_level = access_level
@property
@abstractmethod
def name(self) -> str:
"""Attack name for logging/display."""
pass
@abstractmethod
def run(
self,
target_split: str = "target_test",
num_target_models: Optional[int] = None,
verbose: bool = True,
) -> Dict[str, np.ndarray]:
"""
Run the attack on a target split.
Args:
target_split: Split to attack (target_train, target_val, target_test)
num_target_models: Number of target models (None = all)
verbose: Print progress
Returns:
Dictionary with:
'scores': (num_samples,) aggregated membership scores
'labels': (num_samples,) true membership labels
'per_model_scores': (num_samples, num_models) individual scores
"""
pass
def get_shadow_data(self) -> Dict[str, np.ndarray]:
"""
Get shadow model data for attack calibration.
Returns:
Dictionary with shadow model losses and membership matrix.
"""
shadow_losses = self.prediction_cache.store["shadow"]["losses"][:, :]
shadow_membership = self.experiment.get_membership_matrix("shadow").matrix
return {
"losses": shadow_losses,
"membership": shadow_membership,
}
def get_target_data(
self, split: str, num_models: Optional[int] = None
) -> Dict[str, np.ndarray]:
"""
Get target model data for the attack.
Args:
split: Target split name
num_models: Number of models (None = all)
Returns:
Dictionary with target losses, logits, and membership.
"""
membership_matrix = self.experiment.get_membership_matrix(split)
if num_models is None:
num_models = membership_matrix.num_models
target_losses = self.prediction_cache.store[split]["losses"][:, :num_models]
target_logits = self.prediction_cache.store[split]["logits"][:, :num_models, :]
return {
"losses": target_losses,
"logits": target_logits,
"membership": membership_matrix,
"num_models": num_models,
}
def compute_in_out_statistics(
self,
shadow_losses: np.ndarray,
shadow_membership: np.ndarray,
) -> Dict[str, np.ndarray]:
"""
Compute IN/OUT loss statistics from shadow models.
Args:
shadow_losses: (num_samples, num_shadow_models)
shadow_membership: (num_samples, num_shadow_models) boolean
Returns:
Dictionary with in_mean, in_std, out_mean, out_std
"""
in_mask = shadow_membership
out_mask = ~shadow_membership
in_losses_masked = np.where(in_mask, shadow_losses, np.nan)
out_losses_masked = np.where(out_mask, shadow_losses, np.nan)
return {
"in_mean": np.nanmean(in_losses_masked, axis=1),
"in_std": np.nanstd(in_losses_masked, axis=1) + 1e-10,
"out_mean": np.nanmean(out_losses_masked, axis=1),
"out_std": np.nanstd(out_losses_masked, axis=1) + 1e-10,
}
class GreyBoxAttack(BaseAttack):
"""
Base class for grey-box attacks with activation access.
Extends BaseAttack with methods to load and use activation features.
"""
def __init__(
self,
experiment: MIAExperiment,
prediction_cache: PredictionCache,
activation_cache=None,
):
super().__init__(experiment, prediction_cache, access_level="greybox")
self.activation_cache = activation_cache
def get_shadow_activations(self) -> np.ndarray:
"""Get shadow model activations."""
if self.activation_cache is None:
raise ValueError("Activation cache not provided")
return self.activation_cache.store["shadow"]["features"][:, :]
def get_target_activations(
self, split: str, num_models: Optional[int] = None
) -> np.ndarray:
"""Get target model activations."""
if self.activation_cache is None:
raise ValueError("Activation cache not provided")
if num_models is None:
return self.activation_cache.store[split]["features"][:, :]
return self.activation_cache.store[split]["features"][:, :num_models, :]
def compute_activation_statistics(
self,
shadow_activations: np.ndarray,
shadow_membership: np.ndarray,
) -> Dict[str, np.ndarray]:
"""
Compute IN/OUT activation statistics from shadow models.
Args:
shadow_activations: (num_samples, num_shadow_models, feature_dim)
shadow_membership: (num_samples, num_shadow_models) boolean
Returns:
Dictionary with in_act_mean, in_act_std, out_act_mean, out_act_std
"""
in_mask = shadow_membership[:, :, np.newaxis] # (N, M, 1)
out_mask = ~shadow_membership[:, :, np.newaxis]
in_act_masked = np.where(in_mask, shadow_activations, np.nan)
out_act_masked = np.where(out_mask, shadow_activations, np.nan)
return {
"in_act_mean": np.nanmean(in_act_masked, axis=1), # (N, D)
"in_act_std": np.nanstd(in_act_masked, axis=1) + 1e-10,
"out_act_mean": np.nanmean(out_act_masked, axis=1),
"out_act_std": np.nanstd(out_act_masked, axis=1) + 1e-10,
}
class WhiteBoxAttack(GreyBoxAttack):
"""
Base class for white-box attacks with gradient access.
Extends GreyBoxAttack with methods to load and use gradient features.
"""
def __init__(
self,
experiment: MIAExperiment,
prediction_cache: PredictionCache,
activation_cache=None,
gradient_cache=None,
):
super().__init__(experiment, prediction_cache, activation_cache)
self.access_level = "whitebox"
self.gradient_cache = gradient_cache
def get_shadow_gradients(self) -> Dict[str, np.ndarray]:
"""Get shadow model gradient features."""
if self.gradient_cache is None:
raise ValueError("Gradient cache not provided")
grp = self.gradient_cache.store["shadow"]
return {
"grad_norms": grp["grad_norms"][:, :],
"grad_dot_weight": grp["grad_dot_weight"][:, :],
"grad_features": grp["grad_features"][:, :, :],
}
def get_target_gradients(
self, split: str, num_models: Optional[int] = None
) -> Dict[str, np.ndarray]:
"""Get target model gradient features."""
if self.gradient_cache is None:
raise ValueError("Gradient cache not provided")
grp = self.gradient_cache.store[split]
if num_models is None:
return {
"grad_norms": grp["grad_norms"][:, :],
"grad_dot_weight": grp["grad_dot_weight"][:, :],
"grad_features": grp["grad_features"][:, :, :],
}
return {
"grad_norms": grp["grad_norms"][:, :num_models],
"grad_dot_weight": grp["grad_dot_weight"][:, :num_models],
"grad_features": grp["grad_features"][:, :num_models, :],
}
def compute_gradient_statistics(
self,
shadow_grad_norms: np.ndarray,
shadow_membership: np.ndarray,
) -> Dict[str, np.ndarray]:
"""
Compute IN/OUT gradient norm statistics from shadow models.
Args:
shadow_grad_norms: (num_samples, num_shadow_models)
shadow_membership: (num_samples, num_shadow_models) boolean
Returns:
Dictionary with in_grad_mean, in_grad_std, out_grad_mean, out_grad_std
"""
in_mask = shadow_membership
out_mask = ~shadow_membership
in_grad_masked = np.where(in_mask, shadow_grad_norms, np.nan)
out_grad_masked = np.where(out_mask, shadow_grad_norms, np.nan)
return {
"in_grad_mean": np.nanmean(in_grad_masked, axis=1),
"in_grad_std": np.nanstd(in_grad_masked, axis=1) + 1e-10,
"out_grad_mean": np.nanmean(out_grad_masked, axis=1),
"out_grad_std": np.nanstd(out_grad_masked, axis=1) + 1e-10,
}
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