  Adversarial patch attacks, crafted to compromise the integrity of Deep Neural
Networks (DNNs), significantly impact Artificial Intelligence (AI) systems
designed for object detection and classification tasks. The primary purpose of
this work is to defend models against real-world physical attacks that target
object detection and classification. We analyze attack techniques and propose a
robust defense approach. We successfully reduce model confidence by over 20%
using adversarial patch attacks that exploit object shape, texture and
position. Leveraging the inpainting pre-processing technique, we effectively
restore the original confidence levels, demonstrating the importance of robust
defenses in mitigating these threats. Following fine-tuning of an AI model for
traffic sign classification, we subjected it to a simulated pixelized
patch-based physical adversarial attack, resulting in misclassifications. Our
inpainting defense approach significantly enhances model resilience, achieving
high accuracy and reliable localization despite the adversarial attacks. This
contribution advances the resilience and reliability of object detection and
classification networks against adversarial challenges, providing a robust
foundation for critical applications.
