adv_gan

Generating Adversarial Examples with Adversarial Networks

Paper | IJCAI 2018

Usage

Inference

$ python3 advgan.py --img images/0.jpg --target 4 --model Model_C --bound 0.3

Each of these settings has a separate Generator trained. This code loads appropriate trained model from saved/ directory based on given arguments. As of now there are 22 Generators for different targets, different bounds (0.2 and 0.3) and target models (only Model_C for now).

Training AdvGAN (Untargeted)

$ python3 train_advgan.py --model Model_C --gpu

Training AdvGAN (Targeted)

$ python3 train_advgan.py --model Model_C --target 4 --thres 0.3 --gpu
# thres: Perturbation bound 

Use --help for other arguments available (epochs, batch_size, lr etc.)

Training Target Models (Models A, B and C)

$ python3 train_target_models.py --model Model_C

For TensorBoard visualization,

$ python3 generators.py
$ python3 discriminators.py

This code supports only MNIST dataset for now. Same notations as in paper are followed (mostly).

Results

There are few changes that have been made for model to work.

• Generator in paper has ReLU on the last layer. If input data is normalized to [-1 1] there wouldn't be any perturbation in the negative region. As expected accuracies were poor (~10% Untargeted). So ReLU was removed. Also, data normalization had significat effect on performance. With [-1 1] accuracies were around 70%. But with [0 1] normalization accuracies were ~99%.
• Perturbations (pert) and adversarial images (x + pert) were clipped. It's not converging otherwise.

These results are for the following settings.

• Dataset - MNIST
• Data normalization - [0 1]
• thres (perturbation bound) - 0.3 and 0.2
• No ReLU at the end in Generator
• Epochs - 15
• Batch Size - 128
• LR Scheduler - step_size 5, gamma 0.1 and initial lr - 0.001

Target	Acc [thres: 0.3]	Acc [thres: 0.2]
Untargeted	0.9921	0.8966
0	0.9643	0.4330
1	0.9822	0.4749
2	0.9961	0.8499
3	0.9939	0.8696
4	0.9833	0.6293
5	0.9918	0.7968
6	0.9584	0.4652
7	0.9899	0.6866
8	0.9943	0.8430
9	0.9922	0.7610

Untargeted

Pred: 9	Pred: 3	Pred: 8	Pred: 8	Pred: 4	Pred: 3	Pred: 8	Pred: 3	Pred: 3	Pred: 8

Targeted

Target: 0	Target: 1	Target: 2	Target: 3	Target: 4	Target: 5	Target: 6	Target: 7	Target: 8	Target: 9
Pred: 0	Pred: 1	Pred: 2	Pred: 3	Pred: 4	Pred: 5	Pred: 6	Pred: 7	Pred: 8	Pred: 9
Pred: 0	Pred: 1	Pred: 2	Pred: 3	Pred: 4	Pred: 5	Pred: 6	Pred: 7	Pred: 8	Pred: 9
Pred: 0	Pred: 1	Pred: 2	Pred: 3	Pred: 4	Pred: 5	Pred: 6	Pred: 7	Pred: 8	Pred: 9