Err on the Side of Texture: Texture Bias on Real Data Article Swipe

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Blaine Hoak , Ryan Sheatsley , Patrick McDaniel ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2412.10597

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.

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Computer Science

Artificial Intelligence

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Texture (cosmology) Computer science Artificial intelligence Pattern recognition (psychology) Image (mathematics)

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2412.10597
PDF: https://arxiv.org/pdf/2412.10597
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4405468204

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DOI: https://doi.org/10.48550/arxiv.2412.10597

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Title: Err on the Side of Texture: Texture Bias on Real Data

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-12-13

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Authors: Blaine Hoak, Ryan Sheatsley, Patrick McDaniel

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Landing page: https://arxiv.org/abs/2412.10597

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PDF URL: https://arxiv.org/pdf/2412.10597

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Open access: Yes

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OA URL: https://arxiv.org/pdf/2412.10597

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Concepts: Texture (cosmology), Computer science, Artificial intelligence, Pattern recognition (psychology), Image (mathematics)

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Cited by: 0

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Related works (count): 10

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