- A Marauder's Map of Security and Privacy in Machine Learning
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- Naveed Akhtar, Ajmal Mian
- IEEE Access, 2018, journal
- Paper
- Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
- Towards the Science of Security and Privacy in Machine Learning
- Membership Inference Attacks against Machine Learning Models
- Reza Shokri, Marco Stronati, Congzheng Song, Vitaly Shmatikov
- S&P, 2017, conference
- Paper
- Adversarial examples in the physical world
- A Kurakin, I Goodfellow, S Bengio
- ICLR, 2017, conference workshop
- Paper
- Practical Black-Box Attacks against Machine Learning
- Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z.Berkay Celik, Ananthram Swami
- Asia CCS, 2017, conference
- Paper
- Stealing Machine Learning Models via Prediction APIs
- Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, Thomas Ristenpart
- USENIX Security, 2016, conference
- Paper
- Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
- Nicolas Papernot, Patrick McDaniel, Ian Goodfellow
- arXiv, 2016, preprint
- Paper
- Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
- Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami
- S&P, 2016, conference
- Paper
- Explaining and Harnessing Adversarial Examples
- Ian Goodfellow, Jonathon Shlens, Christian Szegedy
- ICLR, 2015, conference
- Paper
- Intriguing properties of neural networks
- Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, etc.
- ICLR, 2014, conference
- Paper
- Multi-task Memory Networks for Category-specific Aspect and Opinion Terms Co-extraction
- Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction∗