FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
Paper 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.
Cases of Digital Attack
Carmear distance is 3
Carmear distance is 5
Carmear distance is 10
Cases of Multi-view Attack
The first row is the original detection result. The second row is the camouflaged detection result.
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 |
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