Episode 13 — Adversarial Evasion
This episode introduces adversarial evasion, a class of attacks in which maliciously crafted inputs cause AI systems to misclassify or behave incorrectly. For exam purposes, learners must be able to define adversarial examples, explain why they are often imperceptible to humans, and distinguish them from poisoning attacks, which occur during training. Evasion attacks take place at inference time and undermine confidence in model reliability. The episode covers historical research origins in image recognition and extends to natural language and audio domains, illustrating the cross-modal nature of the risk.
The applied discussion highlights techniques for generating adversarial inputs, including gradient-based perturbations and black-box query methods. Examples range from modified stop signs that confuse autonomous vehicles to hidden commands embedded in audio targeting voice assistants. Defensive strategies include adversarial training, input preprocessing, and anomaly detection, though each has trade-offs in performance and scalability. For certification candidates, the exam relevance lies in recognizing definitions, attack mechanisms, and the limitations of current defenses. Real-world troubleshooting scenarios emphasize challenges of detecting subtle manipulations at runtime, reinforcing the need for layered monitoring and resilience. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.
