Evaluating the Adversarial Vulnerability of YOLOv11 for Indonesian Sign Language (BISINDO) Recognition

Abstract

Indonesian Sign Language (BISINDO) is a common form of communication used by the deaf community in Indonesia. Various deep learning models have been successfully utilized to recognize BISINDO gestures, including object detection models such as YOLO. However, these models remain vulnerable to adversarial attacks. This study aims to evaluate the robustness of the YOLOv11 model against three types of adversarial attacks, namely the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD),

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