From the course: CompTIA SecAI+ (CY0-001) Cert Prep

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Adversarial networks

Adversarial networks

Earlier in this course, we discussed how generative adversarial networks pit a generator model against a discriminator so that the generator gradually learns to create data that looks authentic. Attackers exploit this competitive training loop to produce content that defeats both human scrutiny and automated defenses. By iteratively refining images, audio, or text, a well-trained generator can synthesize outputs that slip past filters, poison downstream models, or mislead decision-making systems. The automotive industry has seen repeated demonstrations of this threat. Security researchers added small stickers to roadside speed limit signs and caused an image recognition system to read 35 mph as 85 mph, which could have been potentially catastrophic in a real video. Follow-on studies mounted dynamic adversarial patches on a second car's digital display, an attacker alter what a nearby autonomous vehicle sees in real-time. Attackers have used similar techniques to undermine online…

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