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README.md

What is this repo about?

This is a top2 winning solution for this adversarial competition

pipeline

For more details about the competition and solution please also be sure to see

Solution authors:

Further reading

1. Build docker image

To build the docker image from the Dockerfile located in dockerfile please do:

cd dockerfile
docker build -t face_attack_docker .

Also please make sure that nvidia-docker2 and proper nvidia drivers are installed.

To test the installation run

docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi

Then launch the container as follows:

docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all -it -v /your/folder/:/home/keras/notebook/your_folder -p 8888:8888 -p 6006:6006 --name face_attack --shm-size 16G face_attack_docker

Please note that w/o --shm-size 16G PyTorch dataloader classes will not work. The above command will start a container with a Jupyter notebook server available via port 8888. Port 6006 is for tensorboard, if necessary.

Then you can exec into the container like this. All the scripts were run as root, but they must also work under user keras

docker exec -it --user root 46b9bd3fa3f8 /bin/bash

or

docker exec -it --user keras 46b9bd3fa3f8 /bin/bash

To find out the container ID run

 docker container ls

2. Data structure and preparation

Download this version of the black box into the folder Backup link for the source file

This is the version for

  • Ubuntu 16.04
  • Python 3.5
  • CUDA 9.0 These versions are in the above Dockerfile

I archived a copy of the prepared datasets here - backup. Download each file separately to the /data folder and make sure that file names follow the below convention. Also you will have to unpack the zip files with images to the corresponding folders.

The following files are required

  • data/pairs_list.csv - 1000 5+5 source-target combinations
  • data/img_list_1M.csv - list with 1M images
  • data/img_descriptors_1M.npy - numpy array with 1M descriptors
  • data/student_model_imgs - a folder with 1M images (you will have to unzip it)
  • data/imgs - a folder with original attack images (you will have to unzip it)

The following pre-trained model files are to be located in the followind folders (if you do not want to train them)

  • student_net_learning/checkpoint/resnet34_scale_fold0_best.pth.tar
  • student_net_learning/checkpoint/ResNet50/best_model_chkpt.t7
  • student_net_learning/checkpoint/Xception/best_model_chkpt.t7
  • student_net_learning/checkpoint/resnet18_scale_fold0_best.pth.tar
  • student_net_learning/checkpoint/densenet161_1e4_scale_fold0_best.pth.tar

To download the pre-trained weights you can use the following links:

As an alternative means of downloading data you may visit competition page.

3. Run attack inference

Run attack scripts using the above pre-trained weights, or proceed to training sections

First attack using Fast Gradient Value Method

python attacker.py --root ./data/imgs/ --save_root ./dual_net_new/ --datalist ./data/pairs_list.csv --start_from 0 --attack_type IFGM \
--model_name resnet34 ResNet50 Xception resnet18 densenet161 \
--checkpoint \
student_net_learning/checkpoint/resnet34_scale_fold0_best.pth.tar \
student_net_learning/checkpoint/ResNet50/best_model_chkpt-resnet50.t7 \
student_net_learning/checkpoint/Xception/best_model_chkpt-xception.t7 \
student_net_learning/checkpoint/resnet18_scale_fold0_best.pth.tar \
student_net_learning/checkpoint/densenet161_1e4_scale_fold0_best.pth.tar --cuda

Please note that full inference may take 30+ hours, therefore the easiest way to speed up the script is to run it in several threads using --start_from 0 parameter Then run one instance on one-pixel attack

python attacker.py --root ./dual_net_new --save_root ./dual_net_new_op/ \
--datalist ./data/pairs_list.csv --cuda --start_from 0 --attack_mode continue --attack_type OnePixel

Then run one more instance of one pixel attack

python attacker.py --root ./dual_net_new_op --save_root ./dual_net_new_op_5/ \
--datalist ./data/pairs_list.csv --cuda --start_from 0 --attack_mode continue --attack_type OnePixel --iter 5

Then run one more instance of one pixel attack

python attacker.py --root ./dual_net_new_op_5 --save_root ./dual_net_new_op_15/ \
--datalist ./data/pairs_list.csv --cuda --start_from 0 --attack_mode continue --attack_type OnePixel --iter 10

Then run one more instance of one pixel attack

python attacker.py --root ./dual_net_new_op_15 --save_root ./FINAL_FINAL/ \
--datalist ./data/pairs_list.csv --cuda --start_from 0 --attack_mode continue --attack_type OnePixel-last-hope --iter 5

Then run evaluation script

python evaluate.py --attack_root ./FINAL_FINAL/ --target_dscr ./data/val_descriptors.npy --submit_name final_final --gpu_id 0

4. Train snakers41's CNNs (optional)

Prepare the data

First cd av_cnns

Then make sure that the following files and folders are available via the following relative paths

  • ./data/img_list_1M.csv - list with 1M images
  • ../data/img_descriptors_1M.npy - numpy array with 1M descriptors
  • ../data/student_model_imgs - a folder with 1M images

Train models

Then copy the following scripts one by one to a run.sh file and run sh run.sh To view TensorBoard logs you need to enable TensorBoard via

tensorboard --logdir='path/to/av_cnns/tb_logs' --port=6006

The weights will be saved to weights/ Alternatively you can run all the scripts as one file

Densenet

python3 distill_network.py \
	--arch densenet161 --lognumber densenet161_1e4_scale \
	--epochs 25 --start-epoch 0 \
	--batch-size 256 --workers 10 \
	--val_size 0.1 --do_augs False \
	--lr 1e-4 --ths 1e-2 \
	--m1 5 --m2 15 \
	--optimizer adam --print-freq 10 \
	--tensorboard True \

ResNet34

python3 distill_network.py \
	--arch resnet18 --lognumber resnet18_scale \
	--epochs 25 --start-epoch 0 \
	--batch-size 512 --workers 10 \
	--val_size 0.1 --do_augs False \
	--lr 1e-3 --ths 1e-2 \
	--m1 5 --m2 15 \
	--optimizer adam --print-freq 10 \
	--tensorboard True \

ResNet18

python3 distill_network.py \
	--arch resnet34 --lognumber resnet34_scale \
	--epochs 25 --start-epoch 0 \
	--batch-size 512 --workers 10 \
	--val_size 0.1 --do_augs False \
	--lr 1e-3 --ths 1e-2 \
	--m1 5 --m2 15 \
	--optimizer adam --print-freq 10 \
	--tensorboard True \

Pre-trained weights

To download the pre-trained weights you can use the following links:

Also you can add a -resume flag to start from a checkpoint:

python3 distill_network.py \
	--arch resnet18 --lognumber resnet18_scale \
	--epochs 30 --start-epoch 0 \
	--batch-size 512 --workers 10 \
	--val_size 0.1 --do_augs False \
	--lr 1e-3 --ths 1e-2 \
	--m1 5 --m2 15 \
	--optimizer adam --print-freq 10 \
	--tensorboard True \
	--resume weights/your_weights.pth.tar

Training time

With the above setting on 2x1080Ti training takes:

  • 2 hours for ResNet18
  • 3 hours for ResNet34
  • 11 hours for DenseNet169

training_curves

5. Train mortido's CNNs (optional)

Provided original scripts log w/o alterations The require code from the original repository


======================================
xception redesign
=====================================
python main.py --name Xception --model_name Xception --epochs 6 --down_epoch 2 --cuda --batch_size 64 --datalist ../data/data_list/ --root C:/ --lr 0.001 --finetune --criterion HUBER --resume --max_train_imgs 100000
python main.py --name Xception --model_name Xception --epochs 3 --down_epoch 1 --cuda --batch_size 64 --datalist ../data/data_list/ --root C:/ --lr 0.0001 --finetune --ignore_prev_run --resume --max_train_imgs 100000
python main.py --name Xception --model_name Xception --epochs 2 --down_epoch 1 --cuda --batch_size 64 --datalist ../data/data_list/ --root C:/ --lr 0.0001 --ignore_prev_run --resume --max_train_imgs 100000
(accidentely 3 epochs with frozen layers...)
python main.py --name Xception --model_name Xception --epochs 3 --down_epoch 2 --cuda --batch_size 64 --datalist ../data/data_list/ --root C:/ --lr 0.0001 --finetune --ignore_prev_run --resume --max_train_imgs 100000 
python main.py --name Xception --model_name Xception --epochs 1 --down_epoch 1 --cuda --batch_size 64 --datalist ../data/data_list/ --root C:/ --lr 0.0001 --ignore_prev_run --resume --max_train_imgs 100000
python main.py --name Xception --model_name Xception --epochs 1 --down_epoch 1 --cuda --batch_size 64 --datalist ../data/data_list/ --root C:/ --lr 0.0001 --ignore_prev_run --resume --max_train_imgs 500000
python main.py --name Xception --model_name Xception --epochs 2 --down_epoch 1 --cuda --batch_size 32 --datalist ../data/data_list/ --root C:/ --lr 0.0001 --ignore_prev_run --resume
python main.py --name Xception --model_name Xception --epochs 3 --down_epoch 1 --cuda --batch_size 32 --datalist ../data/data_list/ --root C:/ --lr 0.0005 --ignore_prev_run --resume
python main.py --name Xception --model_name Xception --epochs 2 --cuda --batch_size 32 --datalist ../data/data_list/ --root C:/ --lr 0.000005 --ignore_prev_run --resume
=========================
resnet50
=========================
python main.py --name ResNet50 --model_name ResNet50 --epochs 3 --down_epoch 1 --cuda --batch_size 16 --datalist ../data/data_list/ --root C:/ --lr 0.005 --max_train_imgs 10000
python main.py --name ResNet50 --model_name ResNet50 --epochs 3 --down_epoch 4 --cuda --batch_size 32 --datalist ../data/data_list/ --root C:/ --lr 0.0001 --ignore_prev_run --resume
python main.py --name ResNet50 --model_name ResNet50 --epochs 1 --down_epoch 4 --cuda --batch_size 32 --datalist ../data/data_list/ --root C:/ --lr 0.0001 --ignore_prev_run --resume
python main.py --name ResNet50 --model_name ResNet50 --epochs 1 --down_epoch 4 --cuda --batch_size 32 --datalist ../data/data_list/ --root C:/ --lr 0.00003 --ignore_prev_run --resume
python main.py --name ResNet50 --model_name ResNet50 --epochs 1 --down_epoch 4 --cuda --batch_size 32 --datalist ../data/data_list/ --root C:/ --lr 0.00001 --ignore_prev_run --resume
python main.py --name ResNet50 --model_name ResNet50 --epochs 1 --down_epoch 4 --cuda --batch_size 32 --datalist ../data/data_list/ --root C:/ --lr 0.000003 --ignore_prev_run --resume
python main.py --name ResNet50 --model_name ResNet50 --epochs 1 --down_epoch 4 --cuda --batch_size 32 --datalist ../data/data_list/ --root C:/ --lr 0.000001 --ignore_prev_run --resume
=========================

6. Play with the final version of end-to-end model (optional)

pipeline pipeline

The thing is, in this task an end-to-end model did not really work on the black-box, but performed well on white-box. The same can be said for for CW attacks (see papers below on details).

The idea here was the following:

  • Have 2 losses - L2 norm and SSIM
  • Train and end-to-end model - from 1+5 images to noise
  • Try siamese LinkNet as baseline architecture
  • Use a frozen instance of Student model for loss calculation

If you would like to play with this idea

  • first cd av_cnns
  • then can launch training with this command:
python3 train_attacker_function_loss.py \
	--lognumber resnet18_attacker_to_img_bb --tensorboard True --tensorboard_images True \
	--workers 4 --print-freq 1 \
	--epochs 100 --start-epoch 0 --fold_num 0 \
	--batch-size 20 --val_size 0.25 \
	--do_augs False --do_shuffle True \
	--is_deconv True \
	--noise_multiplier 0.1 \
	--use_running_mean True \
	--lr 1e-2 --m1 20 --m2 40 \

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