How deeply does Sutskever's List cover adversarial input?

Defines the failure and its significance, but does not explain how perturbations are constructed.

Adversarial input

An adversarial example is an input deliberately perturbed in a way that is often imperceptible to humans but causes a model to make an incorrect prediction.

An adversarial example is an input perturbed slightly—often imperceptibly to people—that causes a model to make a confident, incorrect prediction. Such failures expose a gap between strong benchmark performance and robust visual understanding: residual connections and human-level ImageNet results do not guarantee that a CNN will generalize reliably. section 3.6

Synthetic perturbations and real-world brittleness

A subtle input change can fool a network even when people do not notice it. Separately, Google Photos mislabeled images of Black people as “gorillas”—an example of fragile generalization, not an adversarial example. section 3.6

How Sutskever's List covers it

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Also explained in

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LLMs in ProductionThe study and use of inputs or models designed to manipulate, evade, or expose weaknesses in machine-learning systems.
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Generative AI in ActionAn adversarial example is an input modified or crafted to cause a machine-learning model to produce an incorrect or undesirable output.
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Deep Learning with R, Third EditionAn adversarial example is an input deliberately or naturally modified in a small way that causes a machine-learning model to produce an incorrect output.
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Deep Learning with Python, Third EditionInputs intentionally or effectively perturbed to cause a model to make a wrong prediction despite minimal change to the original example.

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Related concepts

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.
TextAttackused by · TextAttack is an open-source Python framework for generating adversarial examples, augmenting data, training NLP models, and evaluating their robustness.
poisoning attackimplemented by · A poisoning attack deliberately corrupts data, a learning process, or model artifacts to compromise a machine-learning system’s behavior or outputs.
robustnessmeasures · Robustness is the ability of a system or model to maintain acceptable behavior or performance despite variations, disturbances, faults, or challenging conditions.
prompt injectionincludes · Prompt injection is an attack that introduces instructions through input or context to manipulate an AI system’s behavior or override its intended instructions.
predicted valueproduces · A predicted value is an estimated outcome or output that a model produces for given input values.

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