Bias significantly undermines both the accuracy and trustworthiness of machine learning models. To date, one of the strongest biases observed in image classification models is texture bias-where models overly rely on texture information rather than shape information. Yet, existing approaches for measuring and mitigating texture bias have not been able to capture how textures impact model robustness in real-world settings. In this work, we introduce the Texture Association Value (TAV), a novel metric that quantifies how strongly models rely on the presence of specific textures when classifying objects. Leveraging TAV, we demonstrate that model accuracy and robustness are heavily influenced by texture. Our results show that texture bias explains the existence of natural adversarial examples, where over 90% of these samples contain textures that are misaligned with the learned texture of their true label, resulting in confident mispredictions.

翻译：偏差显著削弱了机器学习模型的准确性和可信度。迄今为止，在图像分类模型中观察到的最强偏差之一是纹理偏差——模型过度依赖纹理信息而非形状信息。然而，现有的纹理偏差测量与缓解方法未能捕捉纹理在真实场景中如何影响模型鲁棒性。本文提出纹理关联值（TAV），这是一种量化模型在分类物体时对特定纹理依赖程度的新指标。基于TAV，我们证明模型准确性与鲁棒性受纹理影响显著。研究结果表明，纹理偏差解释了自然对抗样本的存在：超过90%的此类样本包含与其真实标签所学纹理不一致的纹理，从而导致模型产生高置信度的错误预测。