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Update README.md

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freeneuro 2021-09-12 18:50:32 +08:00 committed by GitHub
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@ -98,3 +98,12 @@ 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.
#### Different initialization ways
<figure>
<img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/data13311_ori.png?raw=true', alt="original"/>
<img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/data13311_adv_basic.png?raw=true', alt="basic camouflaged vehicle"/>
<img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/data13311_adv_random.png?raw=true', alt="random camouflaged vehicle"/>
<img src = 'https://github.com/winterwindwang/Full-coverage-camouflage-adversarial-attack/blob/gh-pages/assets/data13311_adv_zero.png?raw=true', alt="zero camouflaged vehicle"/>
</figure>