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Computer Vision models has increasingly been embedded into video software to recognize and classify things in the physical world. While this can provide a useful result it also opens the door to vulnerabilities through a physical attack. Using a printed-out generated image, individuals can exploit computer visions models to disguise their true intentions. A possible way to block and mitigate the problems is to detect and blur the entire image to try to allow the AI to inference the said image. 

In this paper, we document our findings from previous research and literature related to adversarial examples and object detection. Artificial Intelligence (AI) is an increasingly powerful tool in various fields, particularly in image classification and object detection. As AI becomes more advanced, new methods to deceive machine learning models, such as adversarial patches, have emerged. These subtle modifications to images can cause AI models to misclassify objects, posing a significant challenge to their reliability. This research builds upon our earlier work by investigating how small patches affect object detection on YOLOv8. Last year, we explored patterns within images and their impact on model accuracy. This study extends that work by testing how adversarial patches, particularly those targeting animal patterns, affect YOLOv8's ability to accurately detect objects. We also explore how untrained patterns influence the model’s performance, aiming to identify weaknesses and improve the robustness of object detection systems. 

With the increased use of machine learning models, there is a need to understand how machine learning models can be maliciously targeted. Understanding how these attacks are ‘enacted’ helps in being able to ‘harden’ models so that it is harder for attackers to evade detection. We want to better understand object detection, the underlying algorithms, different perturbation approaches that can be utilized to fool these models. To this end, we document our findings as a review of existing literature and open-source repositories related to Computer Vision and Object Detection. We also look at how Adversarial Patches impact object detection algorithms. Our objective was to replicate existing processes in order to reproduce results to further our research on adversarial patches.