AI Adversarial Robustness

"AI workflows running in the real world can be vulnerable to adversarial attacks. We’re working to help them resist hacks, rooting out weaknesses, anticipating new strategies, and designing robust models that perform as well in the wild as they do in a sandbox. In short, we need to make AI hack-proof. [...] During development of an AI model, conditions are carefully controlled to obtain the best possible performance — like starting a seedling in a greenhouse. But in the real world, where models are ultimately deployed, conditions are rarely perfect, and risks are abundant. If development is a greenhouse, then deployment is a jungle. We have to prepare AI models to withstand the onslaught. [...] In the real world, AI models can encounter both incidental adversity, such as when data becomes corrupted, and intentional adversity, such as when hackers actively sabotage them. Both can mislead a model into delivering incorrect predictions or results. Adversarial robustness refers to a model’s ability to resist being fooled. Our recent work looks to improve the adversarial robustness of AI models, making them more impervious to irregularities and attacks. We’re focused on figuring out where AI is vulnerable, exposing new threats, and shoring up machine learning techniques to weather a crisis."
== Media ==
=== Articles ===
* What is AI adversarial robustness? - IBM Researchibm.com
=== Videos ===
* How to make AI hack-proof
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* AI; Adversarial; Robustness; Adversarial AI; Superalignment; AI Alignment
* AI Vulnerability; Tech Development; Software Evolution; Software Vulnerablity; Tech Vulnerability
* Adversarial Machine Learning; AI Hacking; AI Termination
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