Object detection models, which are widely used in various domains (such as
retail), have been shown to be vulnerable to adversarial attacks. Existing
methods for detecting adversarial attacks on object detectors have had
difficulty detecting new real-life attacks. We present X-Detect, a novel
adversarial patch detector that can: i) detect adversarial samples in real
time, allowing the defender to take preventive action; ii) provide explanations
for the alerts raised to support the defender's decision-making process, and
iii) handle unfamiliar threats in the form of new attacks. Given a new scene,
X-Detect uses an ensemble of explainable-by-design detectors that utilize
object extraction, scene manipulation, and feature transformation techniques to
determine whether an alert needs to be raised. X-Detect was evaluated in both
the physical and digital space using five different attack scenarios (including
adaptive attacks) and the COCO dataset and our new Superstore dataset. The
physical evaluation was performed using a smart shopping cart setup in
real-world settings and included 17 adversarial patch attacks recorded in 1,700
adversarial videos. The results showed that X-Detect outperforms the
state-of-the-art methods in distinguishing between benign and adversarial
scenes for all attack scenarios while maintaining a 0% FPR (no false alarms)
and providing actionable explanations for the alerts raised. A demo is
available.

本文介绍了一种新的抗干扰贴片检测方法 X-Detect，通过使用可解释的检测器的集成来检测物体检测模型的敌对攻击，并提供解释，以帮助防御者的决策过程。

X-Detect：可解释的零售目标检测中的对抗补丁检测

X-Detect: Explainable Adversarial Patch Detection for Object Detectors  in Retail

Recently, Logic Explained Networks (LENs) have been proposed as
explainable-by-design neural models providing logic explanations for their
predictions. However, these models have only been applied to vision and tabular
data, and they mostly favour the generation of global explanations, while local
ones tend to be noisy and verbose. For these reasons, we propose LENp,
improving local explanations by perturbing input words, and we test it on text
classification. Our results show that (i) LENp provides better local
explanations than LIME in terms of sensitivity and faithfulness, and (ii) logic
explanations are more useful and user-friendly than feature scoring provided by
LIME as attested by a human survey.

该研究提出了一种名为 LENp 的模型，通过扰动输入单词来改进文本分类中的本地解释，其提供的逻辑解释在灵敏度和忠实度方面比 LIME 更好，并且在人员调查中被证明逻辑解释比 LIME 提供的特征评分更有用和用户友好。