Andrade E. D. auteur aut IRD Guerin Joris auteur aut IRD Viterbo J. auteur aut IRD Sampaio I. G. B. auteur aut IRD text journalArticle eng text/pdf born digital access Person Search aims at retrieving a specific individual (the query) within a collection of whole scene images from diverse, non -overlapping cameras. It has the potential to play a pivotal role in various public safety applications like suspect searching and identifying abandoned luggage owners. Person Search encompasses two Computer Vision challenges: 1. Object Detection, which entails localizing humans in whole scene images, and 2. Person ReIdentification, where the query image is compared with images of detected individuals to establish identification. The critical nature of Person Search underscores the imperative to safeguard it against security threats, such as adversarial attacks, which can result in non-detection or misidentification. While adversarial attacks and defense mechanisms have been extensively studied for both Object Detection and Person Re -Identification, there is a noticeable gap in research concerning Person Search. This work presents a comprehensive Systematic Mapping Study and taxonomy of adversarial attacks and defenses in Person Search, utilizing Parsifal and ChatGPT 4 for indepth analysis. We highlight the persistent challenges associated with Person Search and discuss prospects for future advancements in addressing its vulnerabilities. specialized Person search Person re -identification Object detection Adversarial attacks Adversarial defenses 122 020 148 105096 [14 ] 2024 0262-8856 https://www.documentation.ird.fr/hor/fdi:010090790 10.1016/j.imavis.2024.105096 0262-8856 [F B010090790] https://www.documentation.ird.fr/hor/fdi:010090790 https://www.documentation.ird.fr/intranet/publi/2024-08/010090790.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2024-08-28 2025-11-06 fdi:010090790 fre