Demo Abstract: Parameterized Stochastic Ensemble Defense for Object Detection

Yuting Wu, Dongfang Guo, Xiangzhong Luo, Qun Song, Rui Tan

Abstract: Camera-based object detection excels but remains vulnerable to adversarial attacks that suppress target detection (object-hiding attacks). Here, we propose PaSED, a Parameterized Stochastic Ensemble Defense, which leverages HyperNetworks to enable rapid and diverse updates for detection models in the ensemble. At its core, we introduce functional diversity to enhance the defense robustness. It adapts each generation process to the input image preprocessing parameterized by HyperNetworks' random noise input. In our preliminary evaluations against physically deployed attacks, PaSED outperforms five baseline defenses without requiring attack knowledge. It recovers attacked objects in 92% and 98% of frames in the indoor and outdoor testbeds, respectively.