This is the official code for the paper "Ad2Attack: Adaptive Adversarial Attack for Real-Time UAV Tracking".

Overview

Ad^2Attack:Adaptive Adversarial Attack on Real-Time UAV Tracking

Demo video

  • 📹 Our video on bilibili demonstrates the test results of Ad^2Attack on several sequences.

Ad^2Attack

Environment setup

This code has been tested on Ubuntu 18.04, Python 3.8.3, Pytorch 0.7.0/1.6.0, CUDA 10.2. Please install related libraries before running this code:

pip install -r requirements.txt

Attack on Trackers

[SiamAPN]

The pre-trained model of SiamAPN can be found at (epoch=37) : general_model(code:w3u5) and the pre-trained model of Ad^2Attack can be found at /checkpoints/AdATTACK/model.pth

Ad^2Attack on other trackers, e.g., SiamCAR, SiamGAT, HiFT, SiamAPN++ will be released soon.

Datasets Setting

We evaluate our attack method on 3 well-known UAV tracking benchmark, i.e., UAV123, UAV112 and UAVDT You can download them and put them in /pysot/test_dataset remember change the path in Setting.py

Test Attack

vim ~/.bashrc
export PYTHONPATH=/home/user/Ad^2Attack:$PYTHONPATH
export PYTHONPATH=/home/user/Ad^2Attack/pysot:$PYTHONPATH
export PYTHONPATH=/home/user/Ad^2Attack/pix2pix:$PYTHONPATH
source ~/.bashrc
python pysot/tools/test.py 	        \
	--trackername SiamAPN           \ # tracker_name
	--dataset V4RFlight112          \ # dataset_name
	--snapshot snapshot/general_model.pth   # model_path

The testing result will be saved in the results/dataset_name/tracker_name directory.

Evaluation

If you want to evaluate the Ad^2Attack on trackers, please put those results into results directory.

python pysot/tools/eval.py 	                          \
	--tracker_path ./results          \ # result path
	--dataset V4RFlight112            \ # dataset_name
	--tracker_prefix 'general_model'  \ # tracker_name

Contact

If you have any questions, please contact me.

Sihang Li

Email: [email protected]

Acknowledgement

The code is implemented based on pysot, SiamAPN and CSA. We would like to express our sincere thanks to the contributors.

Owner
Intelligent Vision for Robotics in Complex Environment
Adaptive Vision for Robotics in Complex Environment
Intelligent Vision for Robotics in Complex Environment
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