| Unrolling SGD: Understanding Factors Influencing Machine Unlearning | 0 | 0.34 | 2022 |
| A Zest of LIME: Towards Architecture-Independent Model Distances | 0 | 0.34 | 2022 |
| Adversarial examples for network intrusion detection systems. | 0 | 0.34 | 2022 |
| Bad Characters: Imperceptible NLP Attacks | 0 | 0.34 | 2022 |
| Hyperparameter Tuning with Renyi Differential Privacy | 0 | 0.34 | 2022 |
| On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning | 0 | 0.34 | 2022 |
| Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning | 0 | 0.34 | 2022 |
| Increasing the Cost of Model Extraction with Calibrated Proof of Work | 0 | 0.34 | 2022 |
| Markpainting: Adversarial Machine Learning Meets Inpainting | 0 | 0.34 | 2021 |
| Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings | 1 | 0.37 | 2021 |
| CaPC Learning: Confidential and Private Collaborative Learning | 0 | 0.34 | 2021 |
| Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning | 1 | 0.35 | 2021 |
| Accelerating Symbolic Analysis for Android Apps | 0 | 0.34 | 2021 |
| Entangled Watermarks as a Defense against Model Extraction | 0 | 0.34 | 2021 |
| Tempered Sigmoid Activations For Deep Learning With Differential Privacy | 0 | 0.34 | 2021 |
| Proof-of-Learning: Definitions and Practice | 3 | 0.51 | 2021 |
| Sponge Examples: Energy-Latency Attacks on Neural Networks | 1 | 0.36 | 2021 |
| On the Robustness of Cooperative Multi-Agent Reinforcement Learning | 4 | 0.45 | 2020 |
| Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators on Multi-Tenant FPGAs | 1 | 0.41 | 2020 |
| Third International Workshop on Dependable and Secure Machine Learning – DSML 2020 | 0 | 0.34 | 2020 |
| Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations | 0 | 0.34 | 2020 |
| Thieves on Sesame Street! Model Extraction of BERT-based APIs | 1 | 0.35 | 2020 |
| Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. | 1 | 0.35 | 2019 |
| On Evaluating Adversarial Robustness. | 13 | 0.45 | 2019 |
| How Relevant Is the Turing Test in the Age of Sophisbots? | 1 | 0.36 | 2019 |
| Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. | 0 | 0.34 | 2019 |
| MixMatch: A Holistic Approach to Semi-Supervised Learning. | 0 | 0.34 | 2019 |
| Making machine learning robust against adversarial inputs. | 25 | 1.48 | 2018 |
| Detection under Privileged Information. | 0 | 0.34 | 2018 |
| Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning. | 19 | 0.61 | 2018 |
| Adversarial Vision Challenge. | 0 | 0.34 | 2018 |
| Adversarial Examples that Fool both Human and Computer Vision. | 0 | 0.34 | 2018 |
| Adversarial Examples that Fool both Computer Vision and Time-Limited Humans. | 10 | 0.52 | 2018 |
| Scalable Private Learning with PATE. | 17 | 0.66 | 2018 |
| A Marauder's Map of Security and Privacy in Machine Learning. | 0 | 0.34 | 2018 |
| A Marauder's Map of Security and Privacy in Machine Learning - An overview of current and future research directions for making machine learning secure and private. | 1 | 0.38 | 2018 |
| The Space of Transferable Adversarial Examples. | 42 | 1.29 | 2017 |
| Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. | 41 | 1.11 | 2017 |
| Practical Black-Box Attacks against Machine Learning. | 365 | 11.37 | 2017 |
| Extending Defensive Distillation. | 0 | 0.34 | 2017 |
| Adversarial Attacks on Neural Network Policies. | 46 | 1.87 | 2017 |
| Ensemble Adversarial Training: Attacks and Defenses. | 147 | 3.64 | 2017 |
| Adversarial Examples For Malware Detection | 60 | 1.75 | 2017 |
| On the (Statistical) Detection of Adversarial Examples. | 63 | 2.07 | 2017 |
| On the Protection of Private Information in Machine Learning Systems: Two Recent Approches. | 4 | 0.40 | 2017 |
| Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples. | 152 | 5.55 | 2016 |
| Crafting adversarial input sequences for recurrent neural networks | 44 | 1.68 | 2016 |
| SoK: Security and Privacy in Machine Learning | 18 | 0.61 | 2016 |
| Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples. | 41 | 3.55 | 2016 |
| Machine Learning in Adversarial Settings. | 20 | 0.85 | 2016 |
