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Kompletny słownik sztucznej inteligencji

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kategorie
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podkategorie
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pojęcia
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Model Extraction

Attack where an adversary recreates a machine learning model by querying the target model's API and using the responses to train a substitute model with equivalent capabilities.

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Membership Inference Attack

Attack technique aimed at determining whether a specific data sample was used in a model's training dataset, thereby revealing information about private training data.

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Model Inversion Attack

Attack that approximately reconstructs training data characteristics by exploiting model outputs and prediction information to reverse the learning process.

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Adversarial Examples

Inputs specially designed to deceive a machine learning model, exploiting the model's vulnerabilities to cause incorrect predictions while remaining imperceptible to humans.

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Data Poisoning Attack

Attack where an adversary deliberately inserts malicious data into the training dataset to compromise model performance or create exploitable backdoors.

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Model Stealing

Process by which an attacker illicitly extracts or replicates a proprietary machine learning model by exploiting information accessible through its API or predictive behavior.

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Property Inference Attack

Attack aimed at inferring global properties of the training dataset, such as class distributions or correlations, without directly accessing the data.

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Model Watermarking

Intellectual property technique that embeds invisible markers in a machine learning model to identify and prove ownership in case of theft or unauthorized reproduction.

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Gradient Leakage

Vulnerability where shared gradients during distributed or federated training can reveal sensitive information about participants' local training data.

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Cryptographic Primitives

Fundamental cryptographic operations such as encryption, decryption, hash functions, and digital signatures used as building blocks to construct complex security protocols.

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