Nataniel Ruiz Gutierrez
158 lines
4.8 KiB
Python
158 lines
4.8 KiB
Python
import copy
|
|
import numpy as np
|
|
from collections import Iterable
|
|
from scipy.stats import truncnorm
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
import defenses.smoothing as smoothing
|
|
|
|
class LinfPGDAttack(object):
|
|
def __init__(self, model=None, device=None, epsilon=0.05, k=30, a=0.01, feat = None):
|
|
self.model = model
|
|
self.epsilon = epsilon
|
|
self.k = k
|
|
self.a = a
|
|
self.loss_fn = nn.MSELoss().to(device)
|
|
self.device = device
|
|
self.feat = feat
|
|
|
|
def perturb(self, X_nat, y, c_trg):
|
|
"""
|
|
Given examples (X_nat, y), returns adversarial
|
|
examples within epsilon of X_nat in l_infinity norm.
|
|
"""
|
|
X = X_nat.clone().detach_()
|
|
|
|
for i in range(self.k):
|
|
# print(i)
|
|
X.requires_grad = True
|
|
output, feats = self.model(X, c_trg)
|
|
|
|
if self.feat:
|
|
# print('self.feat ', self.feat)
|
|
output = feats[self.feat]
|
|
y = np.zeros(output.shape)
|
|
y = torch.FloatTensor(y).to(self.device)
|
|
|
|
self.model.zero_grad()
|
|
loss = -self.loss_fn(output, y)
|
|
loss.backward()
|
|
grad = X.grad
|
|
|
|
X_adv = X + self.a * grad.sign()
|
|
|
|
eta = torch.clamp(X_adv - X_nat, min=-self.epsilon, max=self.epsilon)
|
|
X = torch.clamp(X_nat + eta, min=-1, max=1).detach_()
|
|
|
|
self.model.zero_grad()
|
|
|
|
return X, X - X_nat
|
|
|
|
def perturb_blur(self, X_nat, y, c_trg):
|
|
"""
|
|
Given examples (X_nat, y), returns adversarial
|
|
examples within epsilon of X_nat in l_infinity norm.
|
|
"""
|
|
X = X_nat.clone().detach_()
|
|
X_orig = X_nat.clone().detach_()
|
|
|
|
ks = 11
|
|
sig = 1
|
|
|
|
# preproc = smoothing.AverageSmoothing2D(channels=3, kernel_size=ks).to(self.device)
|
|
preproc = smoothing.GaussianSmoothing2D(sigma=sig, channels=3, kernel_size=ks).to(self.device)
|
|
|
|
# blurred_image = smoothing.AverageSmoothing2D(channels=3, kernel_size=ks).to(self.device)(X_orig)
|
|
blurred_image = smoothing.GaussianSmoothing2D(sigma=sig, channels=3, kernel_size=ks).to(self.device)(X_orig)
|
|
|
|
for i in range(self.k):
|
|
# print(i)
|
|
X.requires_grad = True
|
|
output, feats = self.model.forward_blur(X, c_trg, preproc)
|
|
|
|
if self.feat:
|
|
# print('self.feat ', self.feat)
|
|
output = feats[self.feat]
|
|
y = np.zeros(output.shape)
|
|
y = torch.FloatTensor(y).to(self.device)
|
|
|
|
self.model.zero_grad()
|
|
loss = -self.loss_fn(output, y)
|
|
loss.backward()
|
|
grad = X.grad
|
|
|
|
X_adv = X + self.a * grad.sign()
|
|
|
|
eta = torch.clamp(X_adv - X_nat, min=-self.epsilon, max=self.epsilon)
|
|
X = torch.clamp(X_nat + eta, min=-1, max=1).detach_()
|
|
|
|
self.model.zero_grad()
|
|
|
|
return X, X - X_nat, blurred_image
|
|
|
|
def perturb_blur_iter(self, X_nat, y, c_trg):
|
|
"""
|
|
Given examples (X_nat, y), returns adversarial
|
|
examples within epsilon of X_nat in l_infinity norm.
|
|
"""
|
|
X = X_nat.clone().detach_()
|
|
|
|
ks_gauss = 11
|
|
ks_avg = 3
|
|
sig = 1
|
|
blur_type = 1
|
|
|
|
for i in range(self.k):
|
|
# Declare smoothing layer
|
|
if blur_type == 1:
|
|
preproc = smoothing.AverageSmoothing2D(channels=3, kernel_size=ks_avg).to(self.device)
|
|
elif blur_type == 2:
|
|
preproc = smoothing.GaussianSmoothing2D(sigma=sig, channels=3, kernel_size=ks_gauss).to(self.device)
|
|
|
|
X.requires_grad = True
|
|
output, feats = self.model.forward_blur(X, c_trg, preproc)
|
|
|
|
if self.feat:
|
|
# print('self.feat ', self.feat)
|
|
output = feats[self.feat]
|
|
y = np.zeros(output.shape)
|
|
y = torch.FloatTensor(y).to(self.device)
|
|
|
|
self.model.zero_grad()
|
|
loss = -self.loss_fn(output, y)
|
|
loss.backward()
|
|
grad = X.grad
|
|
|
|
X_adv = X + self.a * grad.sign()
|
|
|
|
eta = torch.clamp(X_adv - X_nat, min=-self.epsilon, max=self.epsilon)
|
|
X = torch.clamp(X_nat + eta, min=-1, max=1).detach_()
|
|
|
|
if blur_type == 1:
|
|
sig += 0.2
|
|
if sig == 3.2:
|
|
blur_type = 2
|
|
sig = 1
|
|
if blur_type == 2:
|
|
ks_avg += 2
|
|
if ks_avg == 11:
|
|
blur_type = 1
|
|
ks_avg = 3
|
|
|
|
print(blur_type, sig, ks_avg)
|
|
|
|
|
|
self.model.zero_grad()
|
|
|
|
return X, X - X_nat
|
|
|
|
def clip_tensor(X, Y, Z):
|
|
# Clip X with Y min and Z max
|
|
X_np = X.data.cpu().numpy()
|
|
Y_np = Y.data.cpu().numpy()
|
|
Z_np = Z.data.cpu().numpy()
|
|
X_clipped = np.clip(X_np, Y_np, Z_np)
|
|
X_res = torch.FloatTensor(X_clipped)
|
|
return X_res |