diff --git a/notebooks/hugging_face_evasion.ipynb b/notebooks/hugging_face_evasion.ipynb
index bbd87dfba0..cef28b9df8 100644
--- a/notebooks/hugging_face_evasion.ipynb
+++ b/notebooks/hugging_face_evasion.ipynb
@@ -5,7 +5,7 @@
    "id": "8093e27a-33f6-4cd9-a47b-ea94c3d0c514",
    "metadata": {},
    "source": [
-    "# Evasion Attacks on Hugging Face Models using ART\n",
+    "# Evasion Attacks and Defenses on Hugging Face Models using ART\n",
     "\n",
     "In this notebook we will go over how to use ART to perform evasion attacks on a Hugging Face image classifier. We will be fine-tuning a pre-trained Data-efficient Image Transformer (DeiT) model available from Hugging Face on the CIFAR-10 dataset. We will apply the Projected Gradient Descent (PGD) attack on this model using ART functionality. Then we will be performing adversarial training to defend against such evasion attacks.\n",
     "\n",
diff --git a/notebooks/hugging_face_poisoning.ipynb b/notebooks/hugging_face_poisoning.ipynb
index c309f9efd5..55a2f728ee 100644
--- a/notebooks/hugging_face_poisoning.ipynb
+++ b/notebooks/hugging_face_poisoning.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Poisoning Hugging Face Models using ART\n",
+    "# Dealing with Poisoning Threats of Hugging Face Models using ART\n",
     "\n",
     "In this notebook, we will go over how to use ART to poison a Hugging Face image classifier. We will be applying the dirty label backdoor attack (DLBD) on the Imagenette dataset and fine-tuning a pre-trained Data-efficient Image Transformer (DeiT) model available from Hugging Face.\n",
     "\n",