Merge pull request #2 from Koukyosyumei/same_api

Same api
Koukyosyumei · Jun 20, 2021 · 98c0f5e · 98c0f5e
2 parents 1242148 + ea3ff65
commit 98c0f5e
Show file tree

Hide file tree

Showing 18 changed files with 203 additions and 1,186 deletions.
diff --git a/example/adversarial_example/example_evasion_attack_svm.ipynb b/example/adversarial_example/example_evasion_attack_svm.ipynb
@@ -155,7 +155,7 @@
    "outputs": [],
    "source": [
     "X_minus_1 = X_train[np.where(y_train == \"3\")]\n",
-    "attacker = Evasion_attack_sklearn(clf = clf, X_minus_1 = X_minus_1,\n",
+    "attacker = Evasion_attack_sklearn(target_model = clf, X_minus_1 = X_minus_1,\n",
     "                          dmax =  (5000 / 255) * 2.5,\n",
     "                          max_iter = 300,\n",
     "                          gamma = 1 / (X_train.shape[1] * np.var(X_train)),\n",

diff --git a/example/adversarial_example/example_poison_attack.ipynb b/example/adversarial_example/example_poison_attack.ipynb
@@ -131,7 +131,7 @@
     "plt.title(\"poisoning attack against SVM\")\n",
     "plt.xlabel(\"num of iterations\")\n",
     "plt.ylabel(\"accuracy on validation data\")\n",
-    "plt.savefig(\"poison_loss.png\")\n",
+    "# plt.savefig(\"poison_loss.png\")\n",
     "plt.show()"
    ]
   },
@@ -143,7 +143,7 @@
    "source": [
     "plt.imshow(xc_attacked.reshape(28,28),cmap='gray')\n",
     "plt.title(\"After Attack\")\n",
-    "plt.savefig(\"poison_example.png\")\n",
+    "# plt.savefig(\"poison_example.png\")\n",
     "plt.show()"
    ]
   },

diff --git a/example/membership_inference/membership_inference_CIFAR10.ipynb b/example/membership_inference/membership_inference_CIFAR10.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "subtle-teddy",
+   "id": "round-northern",
    "metadata": {
     "id": "crucial-backup"
    },
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "legal-sister",
+   "id": "indian-candy",
    "metadata": {
     "id": "7XFU8Q1i9OUt"
    },
@@ -43,7 +43,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "anonymous-junction",
+   "id": "upper-cambridge",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "novel-albany",
+   "id": "cheap-heath",
    "metadata": {
     "id": "still-column"
    },
@@ -70,7 +70,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "racial-friend",
+   "id": "cloudy-investing",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -89,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "coral-cedar",
+   "id": "terminal-couple",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -112,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "flush-montgomery",
+   "id": "organized-visitor",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -144,7 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "shaped-gamma",
+   "id": "dedicated-minister",
    "metadata": {
     "id": "norwegian-needle"
    },
@@ -163,7 +163,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "developmental-survival",
+   "id": "regulated-details",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "provincial-newark",
+   "id": "brilliant-occasion",
    "metadata": {
     "id": "reserved-month"
    },
@@ -189,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "vulnerable-peter",
+   "id": "small-rebecca",
    "metadata": {
     "id": "special-sperm"
    },
@@ -247,7 +247,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "treated-responsibility",
+   "id": "finished-mathematics",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -270,6 +270,7 @@
     "        inputs, labels = data\n",
     "        inputs = inputs.to(device)\n",
     "        labels = labels.to(device)\n",
+    "        labels = labels.to(torch.int64)\n",
     "        # zero the parameter gradients\n",
     "        optimizer.zero_grad()\n",
     "\n",
@@ -340,7 +341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "limited-kidney",
+   "id": "excellent-emission",
    "metadata": {
     "id": "DmKyjcOI9Wy5"
    },
@@ -351,7 +352,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "innovative-individual",
+   "id": "stable-spencer",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -384,7 +385,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "surface-daughter",
+   "id": "burning-supplier",
    "metadata": {
     "id": "selected-tournament"
    },
@@ -395,7 +396,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ahead-payment",
+   "id": "statistical-dominant",
    "metadata": {
     "id": "atomic-insulation"
    },
@@ -450,39 +451,30 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "corporate-robertson",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "dmHcP0AB1Fqp",
-    "outputId": "d64a3497-ca7f-417d-dbb0-732e75a258fa"
-   },
+   "id": "fitting-webster",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "shadow_models = [Net().to(device),\r\n",
-    "                  Net().to(device),\r\n",
-    "                  Net().to(device), \r\n",
-    "                  Net().to(device), \r\n",
-    "                  Net().to(device)]\r\n",
-    "shadow_data_size = 2000\r\n",
-    "shadow_transform = transform\r\n",
-    "\r\n",
-    "num_label = 10\r\n",
-    "attack_models = [SVC(probability=True) for i in range(10)]\r\n",
-    "\r\n",
-    "y_test = np.array(y_test)\r\n",
-    "\r\n",
-    "mi = Membership_Inference(shadow_models, attack_models,\r\n",
-    "                          shadow_data_size, shadow_transform)\r\n",
-    "mi.shadow(X_test, y_test, num_itr=20)\r\n",
-    "mi.attack()"
+    "shadow_models = [Net().to(device),\n",
+    "                 Net().to(device)]\n",
+    "shadow_data_size = 2000\n",
+    "shadow_transform = transform\n",
+    "\n",
+    "num_label = 10\n",
+    "attack_models = [SVC(probability=True) for i in range(num_label)]\n",
+    "\n",
+    "y_test = np.array(y_test).astype(np.int64)\n",
+    "\n",
+    "mi = Membership_Inference(victim_net, shadow_models, attack_models,\n",
+    "                          shadow_data_size, shadow_transform)\n",
+    "mi.train_shadow(X_test, y_test, num_itr=1)\n",
+    "mi.train_attacker()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "meaning-spirituality",
+   "id": "further-broadcasting",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -505,7 +497,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "sufficient-cream",
+   "id": "greenhouse-whole",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -535,7 +527,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "attempted-interference",
+   "id": "sustainable-thomson",
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -553,7 +545,7 @@
     "  \r\n",
     "  plt.title(\"overfitting - membership inference performance\")\r\n",
     "  plt.xlabel(\"victim model: trian_accuracy - test_accuracy per class\")\r\n",
-    "  plt.ylabel(\"attack model: auc per class\")\r\n",
+    "  plt.ylabel(\"attack model: auc per clas# s\")\r\n",
     "\r\n",
     "  plt.savefig(\"membership_inference_overfitting.png\")"
    ]
@@ -580,7 +572,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

diff --git a/example/membership_inference/membership_inference_overfitting.png b/example/membership_inference/membership_inference_overfitting.png