Reflection of Lecture 4 Adversarial Robustnes at Stanford CS230 by Rafael Islamuratov

Part 1: Adversarial Robustness The first half of the lecture examines the three waves of adversarial attacks that have emerged over the last decade
:
Adversarial Examples: How imperceptible perturbations—small tweaks to pixels—can fool a vision model into misclassifying a cat as an iguana
.
Backdoor & Data Poisoning Attacks: The risks of malicious triggers hidden in training data that allow attackers to bypass algorithms in production
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Prompt Injections: Modern techniques used to "jailbreak" large language models (LLMs) to override their original instructions
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Math & Defenses: An exploration of why high-dimensional neural networks are sensitive to these attacks and how to build defenses like adversarial training, input sanitization, and red teaming
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Part 2: Generative Modeling The second half shifts to how AI learns the underlying distribution of real-world data to generate images, video, and code
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Generative Adversarial Networks (GANs): Understanding the "minimax game" where a Generator and a Discriminator compete to create realistic data
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Diffusion Models: A breakdown of the technology powering state-of-the-art products like Sora and VO
. The lecture covers the forward diffusion process (adding noise) and the denoising process (reconstructing images from noise)
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Latent Diffusion & Video: Insights into how Latent Diffusion Models manage computational weight by operating in lower-dimensional spaces and how video models add a temporal dimension to spatial noise to create consistent motion
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