A global adversarial example generation method based on class gradients in a single picture and, in a wider scope, a proportion of distributed data in the dataset. The designed global perturbation can affect all data in the dataset. The performance of the proposed method in the PascalVOC and MS-COCO-keypoints datasets is better than that of the existing methods.

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Admin Panels Global_Categorywise_Attack
Overview

Global_-Category-wise_Attack

Getting Started

This document provides tutorials of Global Catergory-wise Attack (GCA). Basically it contains two parts:

  1. The installation procedures of the target network CenterNet, more details can be found at Object as Points
  2. The installation procedures of GCA.
  3. Make sure you have finished Installation Procedures and Datasets before started.

Networks download

Download the models you want to evaluate from the model zoo and put them in Root_File/models/.

##Evaluation

Detection

To evaluate PascalVOC object detection

> Clean outputs:
> run:
python test.py ctdet --exp_id pascal_dla_1x_clean --dataset pascal --arch dla_34 --not_prefetch_test --load_model ../models/ctdet_pascal_dla_512.pth

> Adversarial outputs (GCA):
> run:
python test_universal.py ctdet --exp_id pascal_dla_1x_Uniattack --dataset pascal --arch dla_34 --not_prefetch_test --load_model ../models/ctdet_pascal_dla_512.pth

Pose estimation

> Clean outputs:
> run:
python test.py multi_pose --exp_id coco_resdcn18_clean --dataset coco_hp --arch dla_34 --not_prefetch_test --load_model ../models/multi_pose_dla_1x.pth

> Adversarial outputs (GCA):
> run:
python test_universal.py multi_pose --exp_id coco_resdcn18_attack_uni --dataset coco_hp --arch dla_34 --not_prefetch_test --load_model ../models/multi_pose_dla_1x.pth
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