Human perception of an object’s skeletal structure is particularly robust to diverse perturbations of shape. This skeleton representation possesses substantial advantages for parts-based and invariant shape encoding, which is essential for object recognition. Multiple deep learning-based skeleton detection models have been proposed, while their robustness to adversarial attacks remains unclear. (1) This paper is the first work to study the robustness of deep learning-based skeleton detection against adversarial attacks, which are only slightly unlike the original data but still imperceptible to humans. We systematically analyze the robustness of skeleton detection models through exhaustive adversarial attacking experiments. (2) We propose a novel Frequency attack, which can directly exploit the regular and interpretable perturbations to sharply disrupt skeleton detection models. Frequency attack consists of an excitatory-inhibition waveform with high frequency attribution, which confuses edge-sensitive convolutional filters due to the sudden contrast between crests and troughs. Our comprehensive results verify that skeleton detection models are also vulnerable to adversarial attacks. The meaningful findings will inspire researchers to explore more potential robust models by involving explicit skeleton features.

人类对物体骨骼结构的感知对于形状的各种扰动特别有力。该骨架表示对于基于零件的不变形状编码具有实质性优势，这对于对象识别至关重要。已经提出了多种基于深度学习的骨架检测模型，但它们对对抗攻击的鲁棒性仍不清楚。（1）本文是研究基于深度学习的骨架检测对抗对抗攻击的鲁棒性的第一篇论文，该算法仅与原始数据略有不同，但对人类仍然不为人知。我们通过详尽的对抗攻击实验系统地分析了骨架检测模型的鲁棒性。（2）我们提出一种新颖的频率攻击，它可以直接利用常规和可解释的扰动来急剧破坏骨骼检测模型。频率攻击由具有高频归因的兴奋抑制波形组成，由于波峰和波谷之间的突然对比，使边缘敏感卷积滤波器变得混乱。我们的综合结果证明，骨架检测模型也容易受到对抗性攻击。有意义的发现将激发研究人员通过涉及明确的骨架特征来探索更多潜在的鲁棒模型。我们的综合结果证明，骨架检测模型也容易受到对抗性攻击。有意义的发现将激发研究人员通过涉及明确的骨架特征来探索更多潜在的鲁棒模型。我们的综合结果证明，骨架检测模型也容易受到对抗性攻击。有意义的发现将激发研究人员通过涉及明确的骨架特征来探索更多潜在的鲁棒模型。