YOLO11

Acoustic adversarial attacks on computer vision using audible frequencies disrupt YOLO11 object detection

Researchers demonstrate physical acoustic attacks on AI-based computer vision systems using audible frequencies (<20 kHz), extending prior ultrasonic work to longer effective ranges. By resonating a commercial camera, they induce motion artifacts that cause YOLO11 to misclassify, miss targets, or hallucinate objects. The paper characterizes which image and object features increase vulnerability, offering a foundation for future mitigation strategies. The attack vector is physically realizable against deployed systems including autonomous vehicles and security cameras.