FCA/README.md

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## FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
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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.git).
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### Cases of digital attack
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#### Carmear distance at 3
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<table>
<tr>
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<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_3_elevation_0_ori_pred.gif?raw=true'/></center></td>
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<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_3_elevation_30_ori_pred.gif?raw=true'/></center></td>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_3_elevation_50_ori_pred.gif?raw=true'/></center></td>
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</tr>
<tr>
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<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_3_elevation_0_adv_pred.gif?raw=true'/></center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_3_elevation_30_adv_pred.gif?raw=true'/></center></td>
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<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_3_elevation_50_adv_pred.gif?raw=true'/></center></td>
</tr>
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</table>
#### Carmear distance at 5
<table>
<tr>
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<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_5_elevation_20_ori_pred.gif?raw=true'/></center></td>
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<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_5_elevation_50_ori_pred.gif?raw=true'/></center></td>
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</tr>
<tr>
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<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_5_elevation_20_adv_pred.gif?raw=true'/></center></td>
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<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_5_elevation_50_adv_pred.gif?raw=true'/></center></td>
</tr>
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</table>
#### Carmear distance at 10
<table>
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<tr>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_10_elevation_30_ori_pred.gif?raw=true'/></center></td>
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<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_10_elevation_50_ori_pred.gif?raw=true'/></center></td>
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</tr>
<tr>
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<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_10_elevation_30_adv_pred.gif?raw=true'/></center></td>
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<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/distance_10_elevation_50_adv_pred.gif?raw=true'/></center></td>
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</tr>
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</table>
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### Cases of multi-view robust
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<table>
<tr>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_1.5_elevation_0_6_ori.png?raw=true'/></center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_1.5_elevation_0_42_ori.png?raw=true'/> </center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_1.5_elevation_0_54_ori.png?raw=true'/> </center></td>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_1.5_elevation_0_126_ori.png?raw=true'/> </center></td>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_1.5_elevation_10_330_ori.png?raw=true'/> </center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_1.5_elevation_20_78_ori.png?raw=true'/></center></td>
</tr>
<tr>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_1.5_elevation_0_6_adv.png?raw=true'/></center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_1.5_elevation_0_42_adv.png?raw=true'/> </center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_1.5_elevation_0_54_adv.png?raw=true'/> </center></td>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_1.5_elevation_0_126_adv.png?raw=true'/> </center></td>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_1.5_elevation_10_330_adv.png?raw=true'/> </center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_1.5_elevation_20_78_adv.png?raw=true'/></center></td>
</tr>
</table>
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The first row is the original detection result. The second row is the camouflaged detection result.
<table>
<tr>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_5_elevation_10_57_ori.png?raw=true'/></center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_5_elevation_30_66_ori.png?raw=true'/> </center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_10_elevation_0_135_ori.png?raw=true'/> </center></td>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_10_elevation_20_177_ori.png?raw=true'/> </center></td>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_15_elevation_20_330_ori.png?raw=true'/> </center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_15_elevation_50_327_ori.png?raw=true'/></center></td>
</tr>
<tr>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_5_elevation_10_57_adv.png?raw=true'/></center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_5_elevation_30_66_adv.png?raw=true'/> </center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_10_elevation_0_135_adv.png?raw=true'/> </center></td>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_10_elevation_20_177_adv.png?raw=true'/> </center></td>
<td><center> <img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_15_elevation_20_330_adv.png?raw=true'/> </center></td>
<td><center><img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/camera_distance_15_elevation_50_327_adv.png?raw=true'/></center></td>
</tr>
</table>
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The first row is the original detection result. The second row is the camouflaged detection result.
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### Ablation study
#### Different combination of loss terms
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<img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/abaltion_study_loss.png?raw=true'/>
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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.