How deeply does Inside AI cover adversarial attack?

Defines subtle input alteration and illustrates its effect, but does not explain attack-generation methods or defenses.

Adversarial attack

As explained in Inside AI

An adversarial example is an input deliberately altered to cause a machine-learning model to produce an incorrect or unreliable output.

An adversarial attack deliberately alters an input so a machine-learning model produces an incorrect or unreliable result. The changes can be subtle enough to escape ordinary inspection while disrupting the model’s behavior. section 3.4.2

Surveillance-system disruption

Subtle pixel alterations can disrupt a deep-learning surveillance system designed to detect intruders. Creating such attacks may not be straightforward, but the vulnerability raises serious questions for mission-critical uses. section 3.4.2

Adversarial attacks matter because they expose limits in deployment:

They show that strong performance does not automatically make a deep-learning system trustworthy. section 3.4.2
They are especially concerning when errors affect safety or high-stakes decisions. section 3.4.2

How Inside AI covers it

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Related concepts

promptuses · An input, typically containing instructions or context, supplied to an artificial-intelligence model to guide or elicit its output.
uncertaintyproduces · Uncertainty is incomplete knowledge or confidence about a value, state, interpretation, behavior, or outcome.
red-teamingused by · Red-teaming is an adversarial assessment practice that deliberately simulates attacks or uses challenging inputs to identify a system’s vulnerabilities, failures, or unsafe behavior.
adversarial inputproduces · An input crafted, selected, or modified to exploit a system’s weaknesses or induce incorrect, unsafe, or otherwise unintended behavior.

This explainer was generated by Manning's knowledge graph from the text of Inside AI. The section links point to the parts of the book it draws on.