你试试 DistScan，它能检测目标检测模型的后门，不用搞复杂步骤，比以前方法效果强不少。
目标检测模型在安全场景下易遭后门攻击，现有检测手段对场景级攻击效果有限。 DistScan 通过预 NMS 预测分布偏移来识别后门，无需获取模型权重。 实验在 MS-COCO 和 PASCAL VOC 基准上表现突出，平均准确率提升 27.32 个百分点。
Detecting Backdoors in Object Detection via Pre-NMS Prediction Distribution Shift
Object detection models deployed in safety-critical applications remain vulnerable to backdoor attacks that cause targeted misbehaviors when a hidden trigger is present. Existing detection methods either rely on trigger inversion or exploit architecture-specific assumptions, and critically, representative existing methods fail to generalize reliably to scene-level attacks, where a single trigger induces anomalous behavior across all objects in the scene simultaneously. We present DistScan, 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. DistScan aggregates intermediate class predictions over a clean validation set and flags a model as backdoored if the resulting distribution deviates significantly from the training class frequencies, requiring no model weight access, no trigger knowledge, and no additional training. Extensive experiments on MS-COCO and PASCAL VOC across two architectures and three scene-level attack scenarios demonstrate that DistScan substantially outperforms existing methods, improving average detection accuracy over the best-performing applicable baseline by 27.32 percentage points.