An adversarial example attack method based on predicted bounding box adaptive deformation in optical remote sensing images

Abstract

Existing global adversarial attacks are not applicable to real-time optical remote sensing object detectors based on the YOLO series of deep neural networks, which makes it difficult to improve the adversarial robustness of single-stage detectors. The existing methods do not work well enough in optical remote sensing images, which may be due to the mechanism of adversarial perturbations is not suitable. Therefore, an adaptive deformation method (ADM) was proposed to fool the detector into generating wrong predicted bounding boxes. Building upon this, we introduce the Adaptive Deformation Method Iterative Fast Gradient Sign Method (ADM-I-FGSM) and Adaptive Deformation Mechanism Projected Gradient Descent (ADM-PGD) against YOLOv4 and YOLOv5. ADM method can obtain the deformation trend values based on the length-to-width ratio of the prediction box, and the adversarial perturbation trend generated based on these trend values has better adversarial effect. Through experiments, we validate that our approach exhibits a higher adversarial success rate compared to the state-of-the-art methods. We anticipate that our unveiled attack scheme will aid in the evaluation of adversarial resilience of these models.