Adversarial Robustness – Guide

Adversarial robustness focuses on ensuring that AI systems remain reliable and accurate even when faced with deliberately crafted inputs designed to mislead them.

Security Testing – Guide

AI Benchmarking – Guide: This approach provides a framework for systematically evaluating the performance of AI models under various conditions, including those specifically designed to trigger errors or vulnerabilities.

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Attacks: FGSM/PGD/CW, data-poisoning, model-stealing/prompt-injection.

Common attacks include Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Certified White-box attacks. Furthermore, concerns exist around data poisoning – where malicious data is injected into the training set – and model stealing/prompt injection – exploiting vulnerabilities in large language models.

Frequently asked questions

What is adversarial validation?

Adversarial validation involves testing AI systems with intentionally perturbed inputs to assess their robustness and identify potential weaknesses. This often utilizes metrics like accuracy under attack and the sensitivity of the model to small input changes.

How can we monitor anomalies/drift?

Monitoring for anomalies or drift involves continuously tracking the performance of an AI system over time, looking for deviations from its expected behavior. This can be achieved through metrics like prediction error rates and monitoring input data distributions.

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Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

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