CNNs & Adversarial Examples

Introduction

While Convolutional Neural Networks (CNNs) shine in areas like disease detection and self-driving cars, they're susceptible to **adversarial examples.

Understanding Adversarial Examples

Adversarial examples are inputs slightly tweaked to deceive AI. For instance, an altered image might wrongly lead a CNN to misclassify a rhino.

Potential Risks

Key sectors face challenges due to this vulnerability:

• Self-Driving Cars: Misinterpreting traffic signs.
• Medical Imaging: Incorrect tumor evaluations.
• Security Systems: Evading facial recognition.

Project Objective

We'll produce adversarial examples to confuse our model. This exploration emphasizes the necessity to grasp AI weaknesses and robust testing.

Key Takeaway: Advanced AI models, despite their prowess, have blind spots. Identifying and mitigating these is crucial.