Data Fusion For Authentication

Data Fusion for Authentication is a methodological framework that integrates heterogeneous data from multiple sources or analytical techniques to enhance the accuracy and reliability of determining the geographical origin of a product by leveraging complementary information across modalities.

55

Publications

2.1K

Citations

178

Authors

90

Institutions

Publications per year

2017–2026

15

Authors

178

  1. SB
    Samy Bengio

    Google (United States), United States

    3 publications · 151 citations

    3

    h-index

  2. TM
    Takao Murakami

    National Institute of Advanced Industrial Science and Technology, Japan

    2 publications · 22 citations

    2

    h-index

  3. FA
    Fernando Alonso‐Fernandez

    Halmstad University, Sweden

    2 publications · 56 citations

    2

    h-index

  4. CS
    Carlo Sansone

    University of Naples Federico II, Italy

    2 publications · 76 citations

    2

    h-index

  5. PM
    Peter Mason

    Carleton University, Canada

    2 publications · 105 citations

    2

    h-index

1–5 of 178

Venues

  1. Information Fusion

    4 publications · 116 citations

    4

    h-index

  2. Food Control

    2 publications · 62 citations

    2

    h-index

  3. Pattern Recognition Letters

    2 publications · 179 citations

    2

    h-index

  4. Talanta

    2 publications · 74 citations

    2

    h-index

  5. Infoscience (Ecole Polytechnique Fédérale de Lausanne)

    Repository · 2 publications · 58 citations

    2

    h-index

1–5