Notebook

experiments.ipynb

@@ -430,7 +430,7 @@
     }
    ],
    "source": [
-    "attacker = NoAttacker(model, eps=0.05) # no attack\n",
+    "attacker = NoAttacker(model=model, eps=0.05) # no attack\n",
     "defender = NoDefenceDefender(model) # no defense\n",
     "acc = accuracy(attacker, defender, step_size=1)\n",
     "print(f'Accuracy: {acc:.4f}')"
@@ -878,7 +878,26 @@
     "unprotected_acc = []\n",
     "defender = NoDefenceDefender(model)\n",
     "for eps in eps_space:\n",
-    "    attacker = FGSMAttacker(model, eps=eps)\n",
+    "    attacker = FGSMAttacker(model=model, eps=eps)\n",
+    "    # Noise attack:\n",
+    "    #attacker = NoiseAttacker(model=model, eps=eps)\n",
+    "    # PGD attack:\n",
+    "    #attacker = PGDAttacker(model=model, eps=eps, num_steps=10)\n",
+    "    # Carlini and Wagner attack:\n",
+    "    #attacker = CarliniWagnerAttacker(model=model, eps=eps, num_steps=5, lr=0,01)\n",
+    "    # Deep Fool attack:\n",
+    "    #attacker = DeepFoolAttacker(model=model, eps=eps, num_steps=10)\n",
+    "    # Distillation black box attack:\n",
+    "    #student = MLP(\n",
+    "    #              window_size=window_size,\n",
+    "    #              step_size=1,\n",
+    "    #              device='cuda',\n",
+    "    #              hidden_dim=512,\n",
+    "    #              num_epochs=20,\n",
+    "    #              batch_size=512,\n",
+    "    #              lr=0.001,\n",
+    "    #             )\n",
+    "    #attacker = DistillationBlackBoxAttacker(model=model, eps=eps, student=student)\n",
     "    unprotected_acc.append(accuracy(attacker, defender, step_size=10))"
    ]
   },
@@ -1157,7 +1176,17 @@
    ],
    "source": [
     "# Adversarial training on quantized data:\n",
-    "defender = ATQDefender(model)"
+    "defender = ATQDefender(model=model, qbit=8)\n",
+    "# Adversarial training:\n",
+    "#defender = AdversarialTrainingDefender(model=model, attacker=None, lambd=1)\n",
+    "# Defense by quantization:\n",
+    "#defender = QuantizationDefender(model=model, qbit=5)\n",
+    "# Desense by Distillation:\n",
+    "#defender = DistillationDefender(model=model, temp=100)\n",
+    "# Defense by regularization:\n",
+    "#defender = RegularizationDefender(model=model, regularization='l2', lambd=1., h=0.01)\n",
+    "# Defense by autoencoder:\n",
+    "#defender = AutoEncoderDefender(model=model, lr=0.001, training_attacker=None, adv_coeff=1, num_epochs=10, bottleneck_size=3)"
    ]
   },
   {

fdd_defense/defenders/atonquant.py

@@ -1,7 +1,7 @@
 import numpy as np
 import random
 from fdd_defense.defenders.base import BaseDefender
-from fdd_defense.attackers import FGSMAttacker
+from fdd_defense.attackers import FGSMAttacker, PGDAttacker
 from fdd_defense.utils import weight_reset
 from tqdm.auto import trange, tqdm
 import torch
@@ -25,7 +25,7 @@ def __init__(self, model, qbit=8):
             losses = []
             for ts, _, label in tqdm(self.model.dataloader, desc='Steps ...', leave=False):
                 epsilon = random.choice(self.eps)
-                attacker = FGSMAttacker(model, eps=epsilon)
+                attacker = PGDAttacker(model, eps=epsilon)
                 batch_size = ts.shape[0]
                 adv_ts = attacker.attack(ts, label)
                 label = torch.LongTensor(label).to(self.model.device)