Adversarial Attacks in Machine Learning: Basics of ML Security

As machine learning systems become integrated into critical industries, understanding their vulnerabilities is no longer optional. This text-based course introduces you to the world of adversarial machine learning, where tiny, intentional perturbations can completely fool AI models. By studying these weaknesses, you will transition from understanding basic model behavior to thinking like a security researcher, learning how to identify vulnerabilities in vision, text, and voice models, and how to apply modern defensive techniques to protect them. What you'll learn: - Understand core concepts of adversarial machine learning, including black-box and white-box attacks. - Analyze how small perturbations fool computer vision models and image classifiers. - Explore vulnerabilities in natural language processing, including text-based attacks and modern prompt injection techniques. - Examine audio spoofing and adversarial perturbations in voice-recognition systems. - Apply defensive distillation, adversarial training, and input purification to secure your models. - Practice evaluating model robustness using systematic testing methodologies. This course begins with foundational definitions of machine learning security before guiding you through hands-on conceptual exercises and code-based explanations of attacks and defenses. You will progress from theoretical security concepts to practical, text-based walkthroughs of defensive implementation. Designed for beginners, developers, and aspiring security analysts, this course requires only basic familiarity with machine learning concepts and no prior cybersecurity experience. Start reading today to build more resilient and secure machine learning applications.