RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909629930274816 |
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| author | Hull, Matthew Wang, Haoran Lau, Matthew Helbling, Alec Phute, Mansi Zhang, Chao Kira, Zsolt Lunardi, Willian Andreoni, Martin Lee, Wenke Chau, Polo |
| author_facet | Hull, Matthew Wang, Haoran Lau, Matthew Helbling, Alec Phute, Mansi Zhang, Chao Kira, Zsolt Lunardi, Willian Andreoni, Martin Lee, Wenke Chau, Polo |
| contents | Differentiable rendering techniques like Gaussian Splatting and Neural Radiance Fields have become powerful tools for generating high-fidelity models of 3D objects and scenes. Their ability to produce both physically plausible and differentiable models of scenes are key ingredient needed to produce physically plausible adversarial attacks on DNNs. However, the adversarial machine learning community has yet to fully explore these capabilities, partly due to differing attack goals (e.g., misclassification, misdetection) and a wide range of possible scene manipulations used to achieve them (e.g., alter texture, mesh). This survey contributes the first framework that unifies diverse goals and tasks, facilitating easy comparison of existing work, identifying research gaps, and highlighting future directions - ranging from expanding attack goals and tasks to account for new modalities, state-of-the-art models, tools, and pipelines, to underscoring the importance of studying real-world threats in complex scenes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09749 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering Hull, Matthew Wang, Haoran Lau, Matthew Helbling, Alec Phute, Mansi Zhang, Chao Kira, Zsolt Lunardi, Willian Andreoni, Martin Lee, Wenke Chau, Polo Machine Learning Cryptography and Security Computer Vision and Pattern Recognition Differentiable rendering techniques like Gaussian Splatting and Neural Radiance Fields have become powerful tools for generating high-fidelity models of 3D objects and scenes. Their ability to produce both physically plausible and differentiable models of scenes are key ingredient needed to produce physically plausible adversarial attacks on DNNs. However, the adversarial machine learning community has yet to fully explore these capabilities, partly due to differing attack goals (e.g., misclassification, misdetection) and a wide range of possible scene manipulations used to achieve them (e.g., alter texture, mesh). This survey contributes the first framework that unifies diverse goals and tasks, facilitating easy comparison of existing work, identifying research gaps, and highlighting future directions - ranging from expanding attack goals and tasks to account for new modalities, state-of-the-art models, tools, and pipelines, to underscoring the importance of studying real-world threats in complex scenes. |
| title | RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering |
| topic | Machine Learning Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.09749 |