Security and robustness | Episode 11, The Wild Pod

How might an attacker try to manipulate an AI system used for facial recognition?

Summary:

This episode details adversarial attacks and malicious attempts to trick AI systems, focusing on facial recognition as an example.

These attacks include poisoning the training data, manipulating input data (evasion), and stealing the model itself (extraction).

The vulnerability stems from overfitting, lack of robustness, and poor explainability in AI models.

Ultimately, the text stresses the critical need for creating more resilient AI systems that can withstand such manipulation.

The questions to ask:

• What security measures protect AI systems from cyber threats?

• How do adversarial attacks compromise AI system reliability?

• What defense strategies enhance AI model robustness?
  

This conversation was auto-generated with AI. It is an experiment with you in mind.
The purpose of this first podcast series is to consider how we can reverse the current rising tide of threats by shifting our conception of systems adapted to the new paradigm.
Looking forward to your feedback. I appreciate your support and engagement.
Yael

Building safe intelligence systems, a Wild Intelligence’s exclusive series

Deep dive and practice:

Module 1: Foundations of AI safety

Module 2: Bias, fairness, and explainability

Module 3: Security and robustness

Module 4: Human-AI collaboration and governance

Module 5: Emerging challenges and future trends

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Takeaway of security and robustness:

Security and robustness | A Wild Intelligence exclusive series