UAV物体検出における敵対的パッチ攻撃に対するモデル非依存防御
Model Agnostic Defense against Adversarial Patch Attacks on Object Detection in Unmanned Aerial Vehicles
UAVの物体検出に対する敵対的パッチ攻撃を、オクルージョン除去として定式化し、軽量な単段階のモデル非依存防御を提案した。デジタル・物理環境で攻撃成功率を大幅に低減し、処理コストも抑えられることを示した。
著者: Saurabh Pathak, Samridha Shrestha, Abdelrahman AlMahmoud
分類: cs.CV, cs.RO
原文アブストラクト
Object detection forms a key component in Unmanned Aerial Vehicles (UAVs) for completing high-level tasks that depend on the awareness of objects on the ground from an aerial perspective. In that scenario, adversarial patch attacks on an onboard object detector can severely impair the performance of upstream tasks. This paper proposes a novel model-agnostic defense mechanism against the threat of adversarial patch attacks in the context of UAV-based object detection. We formulate adversarial patch defense as an occlusion removal task. The proposed defense method can neutralize adversarial patches located on objects of interest, without exposure to adversarial patches during training. Our lightweight single-stage defense approach allows us to maintain a model-agnostic nature, that once deployed does not require to be updated in response to changes in the object detection pipeline. The evaluations in digital and physical domains show the feasibility of our method for deployment in UAV object detection pipelines, by significantly decreasing the Attack Success Ratio without incurring significant processing costs. As a result, the proposed defense solution can improve the reliability of object detection for UAVs.