🛡️ Adversarial Attacks
Understand vulnerabilities in neural networks and how to defend against them
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0 / 5 completedIntroduction to Adversarial Attacks
🎯 What are Adversarial Examples?
Adversarial examples are carefully crafted inputs designed to fool neural networks into making incorrect predictions. These inputs are created by adding imperceptible perturbations that are invisible to humans but cause the model to misclassify.
Adversarial attacks can compromise autonomous vehicles, facial recognition, malware detection, and more.
🖼️ Classic Example
Original Image
Adversarial
Goodfellow et al., 2014 - Adding imperceptible noise completely fools the classifier
🔬 Key Properties
Imperceptible
Perturbations are so small that humans cannot distinguish adversarial from original images
Targeted
Can force model to predict any specific target class chosen by attacker
Transferable
Examples crafted for one model often fool other models too, even different architectures
Universal
Single perturbation can be applied to any image to cause misclassification
🎭 Attack Scenarios
Autonomous Vehicles
Adversarial stickers on stop signs cause misclassification
Facial Recognition
Adversarial glasses fool authentication systems
Malware Detection
Adversarial code evades ML-based antivirus
Spam Filters
Carefully crafted emails bypass spam detection
🔍 Why Models Are Vulnerable
High-Dimensional Input Space
Tiny perturbations in each dimension accumulate to significant changes in high dimensions
Linear Behavior
Despite non-linear activations, models exhibit linear behavior in many regions
Overfitting to Data Manifold
Models learn decision boundaries too close to training data, vulnerable off-manifold