A Bibliometric Perspective on Adversarial Machine Learning Research
Elizaveta Vitalievna Sokolova · 2025
The objective of the present study is to analyze the thematic directions and research trends in the field of adversarial machine learning, as well as to identify key challenges and opportunities for further development within this domain. To achieve this goal, a bibliometric approach was employed, utilizing the Vosviewer software to analyze the frequency of terms and cluster publications indexed in the OpenAlex database according to thematic proximity. The primary research methods consisted of a retrospective literature review, quantitative analysis of publication activity, and terminology clustering. The analysis revealed key thematic clusters that reflect both the existing challenges and the promising directions in this field. The results obtained demonstrate the high dynamism of research in the field of adversarial machine learning and emphasize the importance of interdisciplinary approaches for addressing the challenges at hand.