A novel backdoor attack that leverages dynamic object interactions in realistic scenarios to activate malicious behavior by hijacking the Non-Maximum Suppression process in object detectors is proposed, demonstrating robust effectiveness.
DistScan is presented, a backdoor detection framework based on a simple but previously unexploited observation: backdoor injection systematically shifts a model's pre-NMS prediction class distribution away from its training class frequencies, even on clean inputs without any trigger present.
Long-Tian Wang, Zheng-Yu Zhao, Chen-Hao Lin et al.· 0 citations
ODPure is proposed, a novel input-stage black-box defense for object detection, which is based on input purification that ensures stable perception flows and provides robust defense against diverse backdoor attacks and trigger types while preserving baseline accuracy.
Li Zeng, Ming-Cheng Duan, Long-Fei Fan et al.· 0 citations
A reverse-training framework is introduced that weakens the trigger-target association, producing low-ASR backdoor models while preserving clean-input performance and exposing a fundamental attacker-defender asymmetry in existing defense paradigms.
This work proposes Z-PEFT, a lightweight meta-classifier that relies exclusively on layer-wise spectral measures for classification, and achieves the best performance while maintaining low and scalable computational cost among weight-space detectors.
Nicola Pitzalis, Donald Shenaj, Giacomo Cignoni et al.· 0 citations
RAMP is proposed, an attack enhancement method that uses a genetic algorithm to optimize reversed adversarial perturbations under black-box access and then injects them through functionality-preserving binary manipulations, which substantially improves attack effectiveness over trigger-only baselines.
Jin-Wen Xin, Dong-Ni Zhang, Chen-Yang Wang et al.· 0 citations
ArchLock is proposed, a dynamic-target architectural backdoor framework designed exclusively for stateful inference deployments that maintain persistent mutable state across requests that exhibits robustness against fine-tuning and pruning and evades three behavioral detectors under the stated stateful deployment assum...