Exploring Object Recognition Vulnerabilities

This chapter examines an experimental setup aimed at assessing the vulnerabilities of object recognition systems using physical toys and the CIFAR-10 dataset. It reveals how lighting variations and added visual inputs significantly impact classification accuracy, raising concerns about the robustness of machine learning models. The discussion stresses the necessity for improved datasets and evaluation methods to enhance the resilience of these systems against adversarial attacks.