## FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack [Paper](https://arxiv.org/abs/2109.07193) FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack Case study of the FCA. The code can be find in [FCA](https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/tree/gh-pages/src). ### Cases of Digital Attack #### Carmear distance is 3

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#### Carmear distance is 5

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#### Carmear distance is 10

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### Cases of Multi-view Attack

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The first row is the original detection result. The second row is the camouflaged detection result.

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The first row is the original detection result. The second row is the camouflaged detection result. ### Ablation study #### Different combination of loss terms

As we can see from the Figure, different loss terms plays different roles in attacking. For example, the camouflaged car generated by `obj+smooth (we omit the smooth loss, and denotes as obj)` can hidden the vehicle successfully, while the camouflaged car generated by `iou` can successfully suppress the detecting bounding box of the car region, and finally the camouflaged car generated by `cls` successfully make the detector to misclassify the car to anther category. #### Different initialization ways

original	basic initialization	random initialization	zero initialization