Posts Tagged ‘AdversarialML’
[DefCon32] On Your Ocean’s 11 Team, I’m the AI Guy (Technically Girl)
Blending the allure of high-stakes gambles with cutting-edge threats, Harriet Farlow, an AI security specialist, navigates the intersection of adversarial machine learning and casino operations. Targeting Canberra Casino, she exposes frailties in emerging AI integrations for surveillance and monitoring. Her exploits disrupt facial recognition, evade detection, and manipulate gameplay, illustrating broader perils in sectors reliant on such systems.
Harriet’s background spans physics, data science, and government intelligence, culminating in founding ML Security Labs. Her focus: deceiving AI to reveal weaknesses, akin to cyber intrusions but tailored to models’ statistical natures.
Casinos, epitomizing surveillance-heavy environments, increasingly adopt AI for identifying threats and optimizing play. Canberra, though modest, mirrors global trends where a few providers dominate, ripe for widespread impacts.
Adversarial attacks perturb inputs subtly, fooling models without human notice. Harriet employs techniques like fast gradient sign methods, crafting perturbations that reduce classification confidence.
Targeting Facial Recognition
Facial systems, crucial for barring excluded patrons, succumb to perturbations. Harriet generates adversarial examples via libraries like Foolbox, adding noise that misclassifies faces.
Tests show 40.4% success in evading matches, but practical adaptations ensure consistent bypasses. This equates to denial-of-service equivalents in AI, disrupting reliability.
Broader implications span medical diagnostics to autonomous navigation, where minor alterations yield catastrophic errors.
Evading Surveillance and Gameplay Monitoring
Surveillance AI detects anomalies; Harriet’s perturbations obscure actions, mimicking wild exploits.
Gameplay AI monitors for advantages; adversarial inputs confuse chip recognition or behavior analysis, enabling undetected strategies.
Interviews with casino personnel reveal heavy reliance on human oversight, despite AI promises. Only 8% of surveyed organizations secure AI effectively, versus 94% using it.
Lessons from the Inflection Point
Casinos transition to AI amid regulatory voids, amplifying risks. Harriet advocates integrating cyber lessons: robust testing beyond accuracy, incorporating security metrics.
Her findings stress governance: people and processes remain vital, yet overlooked. As societies embrace AI surveillance, vulnerabilities threaten equity and safety.
Harriet’s work urges cross-disciplinary approaches, blending cyber expertise with AI defenses to mitigate emerging dangers.
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