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@@ -140,5 +140,7 @@ are robust across any chosen distribution of transformations
 - [ ] [On Detecting Adversarial Perturbations](https://arxiv.org/pdf/1702.04267.pdf) — augment Neural Networks with a small detector subnetwork which performs binary classification on distinguishing genuine data from data containing adversarial perturbations  
 - [ ] [Towards Robust Deep Neural Networks with BANG](https://arxiv.org/pdf/1612.00138.pdf) — Batch Adjusted Network Gradients  
 - [ ] [Universal adversarial perturbations](https://arxiv.org/abs/1610.08401)  
+- [ ] Networks to generate adversarial examples — AdvGAN, [ATNs](https://arxiv.org/pdf/1703.09387.pdf)  
+
 - [ ] Deepfool, JSMA, [CW](https://arxiv.org/pdf/1608.04644.pdf) — math intensive