Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
Document Type
Event
Start Date
25-4-2024 4:00 PM
Description
Advancements in the field of machine learning have led to object detection systems that can approach or even improve upon human performance. Based on deep learning, these systems play a crucial role in many aspects, and continue to be improved on and see expanded adoption. However, these systems are vulnerable to adversarial attacks that rely on targeted noise to spoof detection. Researchers have applied this concept to increase real world adversarial performance by restricting this noise to a patch that can be placed on new images to disrupt object detection. Previous research has focused on patches applied to person recognition (Thys et al. 2019). We focus on the vulnerabilities inherent in remote sensing systems. We develop an adversarial patch to defeat a modern object detection system, YOLOv8. This work demonstrates that remote sensing can still be defeated by an adversarial patch attack and will inform future efforts to develop model robustness against these attacks.
Included in
UR-87 Adversarial Patch Attack in Deep Learning Based Remote Sensing Object Detection Model
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
Advancements in the field of machine learning have led to object detection systems that can approach or even improve upon human performance. Based on deep learning, these systems play a crucial role in many aspects, and continue to be improved on and see expanded adoption. However, these systems are vulnerable to adversarial attacks that rely on targeted noise to spoof detection. Researchers have applied this concept to increase real world adversarial performance by restricting this noise to a patch that can be placed on new images to disrupt object detection. Previous research has focused on patches applied to person recognition (Thys et al. 2019). We focus on the vulnerabilities inherent in remote sensing systems. We develop an adversarial patch to defeat a modern object detection system, YOLOv8. This work demonstrates that remote sensing can still be defeated by an adversarial patch attack and will inform future efforts to develop model robustness against these attacks.