video1.0<iframe src="https://www.loom.com/embed/f2a6f05254024de5803ae4ebefade4fc" frameborder="0" width="1280" height="960" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>9601280Loomhttps://www.loom.com9601280https://cdn.loom.com/sessions/thumbnails/f2a6f05254024de5803ae4ebefade4fc-e2156bd8e3244f75.gif207.3625In this video, I demonstrate an adversarial attack using PyTorch, where I take an image of a panda and add subtle noise to it. The goal is to fool the AI model into misidentifying the image, which I showcase through a side-by-side comparison. I explain the concept of epsilon, which represents the amount of noise added to the pixels. This technique could have significant implications, especially in scenarios where models are relied upon for critical decisions. I plan to create another video soon to delve deeper into this topic.