The Impact of Real-World Drone Conditions on the Reliability of Adversarial Patch Attacks*
Session Number
2
Advisor(s)
Pooya Khorrami, Danielle Sullivan, MIT Lincoln Laboratory
Grade Level
A133
Discipline
Computer Science
Start Date
15-4-2026 11:10 AM
End Date
15-4-2026 11:55 AM
Abstract
Computer vision models are algorithms that classify and interpret images, typically through neural networks trained with machine learning. One common application of computer vision is in drones, which are being used for surveillance, defense, and traffic monitoring. Computer vision models can often be vulnerable to adversarial patches attacks. Adversarial patches are created to deceive object detection systems into causing objects to appear, disappear, or being misclassified. This study examines the performance of adversarial patches in real-world conditions by streaming live drone imagery, with and without adversarial patches, through an object detection model. Research investigates how practical variables such as unusual viewing angles, varying lighting conditions, image compression, and motion blur can affect computer vision models in the classification of everyday objects such as cars and people.
The Impact of Real-World Drone Conditions on the Reliability of Adversarial Patch Attacks*
Computer vision models are algorithms that classify and interpret images, typically through neural networks trained with machine learning. One common application of computer vision is in drones, which are being used for surveillance, defense, and traffic monitoring. Computer vision models can often be vulnerable to adversarial patches attacks. Adversarial patches are created to deceive object detection systems into causing objects to appear, disappear, or being misclassified. This study examines the performance of adversarial patches in real-world conditions by streaming live drone imagery, with and without adversarial patches, through an object detection model. Research investigates how practical variables such as unusual viewing angles, varying lighting conditions, image compression, and motion blur can affect computer vision models in the classification of everyday objects such as cars and people.