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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
### Cases of Digital Attack
#### Carmear distance is 3
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</tr>
</table>
### Cases of multi-view robust
### Cases of Multi-view Attack
@ -107,7 +107,7 @@ The first row is the original detection result. The second row is the camouflage
<img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/abaltion_study_loss.png?raw=true'/>
As we can see from the Figure, different loss term plays different role in attacking. For example, the camouflaged car generated by `obj+smooth (we omit the smooth loss, and denotes as obj)` hardly hidden from the detector, while the camouflaged car generated by `iou` 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.
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