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Grant support

This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-17-1-0287, the EU (FEDER), and the Spanish MINECO under grant TIN 2015-69175-C4-1-R, the Generalitat Valenciana PROMETEOII/2015/013. F. Martinez-Plumed was also supported by INCIBE under grant INCIBEI-2015-27345 (Ayudas para la excelencia de los equipos de investigacion avanzada en ciberseguridad). J. H-Orallo also received a Salvador de Madariaga grant (PRX17/00467) from the Spanish MECD for a research stay at the CFI, Cambridge, and a BEST grant (BEST/2017/045) from the GVA for another research stay at the CFI.

Analysis of institutional authors

Fabra-Boluda, RCorresponding AuthorFerri, CAuthorHernández-Orallo, JAuthorMontes, REditorMartínez-Plumed, FAuthorAlonso, SEditorRamírez-Quintana, MjAuthor

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October 31, 2024
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Proceedings Paper
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Identifying the Machine Learning Family from Black-Box Models

Publicated to:Lecture Notes In Computer Science. 11160 55-65 - 2018-01-01 11160(), DOI: 10.1007/978-3-030-00374-6_6

Authors: Fabra-Boluda, Raul; Ferri, Cesar; Hernandez-Orallo, Jose; Martinez-Plumed, Fernando; Jose Ramirez-Quintana, Maria

Affiliations

Univ Politecn Valencia, DSIC - Author

Abstract

We address the novel question of determining which kind of machine learning model is behind the predictions when we interact with a black-box model. This may allow us to identify families of techniques whose models exhibit similar vulnerabilities and strengths. In our method, we first consider how an adversary can systematically query a given black-box model (oracle) to label an artificially-generated dataset. This labelled dataset is then used for training different surrogate models (each one trying to imitate the oracle's behaviour). The method has two different approaches. First, we assume that the family of the surrogate model that achieves the maximum Kappa metric against the oracle labels corresponds to the family of the oracle model. The other approach, based on machine learning, consists in learning a meta-model that is able to predict the model family of a new black-box model. We compare these two approaches experimentally, giving us insight about how explanatory and predictable our concept of family is.

Keywords

Adversarial machine learningArtificial intelligenceBlack-box modelClassifiersDissimilarity measuresEnsemblesFuel additivesLearning systemsMachine learning familiesMachine learning modelsMeta modelOn-machinesOracle modelSurrogate model

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-07-09:

  • WoS: 2
  • Scopus: 2

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-09:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 7 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Herrera, F) and Last Author (Cordon, O).

the author responsible for correspondence tasks has been Fabra Boluda, Raúl.