Strong Baseline Defenses Against Clean-Label Poisoning Attacks. - Neal Gupta; W. Ronny Huang; Liam Fowl; Chen Zhu 001; Soheil Feizi; Tom Goldstein; John P. Dickerson (CoRR, 2019)

Robust Few-Shot Learning with Adversarially Queried Meta-Learners. - Micah Goldblum; Liam Fowl; Tom Goldstein (CoRR, 2019)

Adversarially Robust Distillation. - Micah Goldblum; Liam Fowl; Soheil Feizi; Tom Goldstein (AAAI, 2020)

Deep k-NN Defense Against Clean-Label Data Poisoning Attacks. - Neehar Peri; Neal Gupta; W. Ronny Huang; Liam Fowl; Chen Zhu 001; Soheil Feizi; Tom Goldstein; John P. Dickerson (ECCV Workshops (1), 2020)

Headless Horseman: Adversarial Attacks on Transfer Learning Models. - Ahmed Abdelkader; Michael J. Curry; Liam Fowl; Tom Goldstein; Avi Schwarzschild; Manli Shu; Christoph Studer; Chen Zhu 001 (ICASSP, 2020)

Understanding Generalization Through Visualizations. - W. Ronny Huang; Zeyad Emam; Micah Goldblum; Liam Fowl; Justin K. Terry; Furong Huang; Tom Goldstein (ICBINB@NeurIPS, 2020)

Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks. - Micah Goldblum; Steven Reich; Liam Fowl; Renkun Ni; Valeriia Cherepanova; Tom Goldstein (ICML, 2020)

Adversarially Robust Few-Shot Learning: A Meta-Learning Approach. - Micah Goldblum; Liam Fowl; Tom Goldstein (NeurIPS, 2020)

MetaPoison: Practical General-purpose Clean-label Data Poisoning. - W. Ronny Huang; Jonas Geiping; Liam Fowl; Gavin Taylor; Tom Goldstein (NeurIPS, 2020)

Random Network Distillation as a Diversity Metric for Both Image and Text Generation. - Liam Fowl; Micah Goldblum; Arjun Gupta; Amr Sharaf; Tom Goldstein (CoRR, 2020)

Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff. - Eitan Borgnia; Valeriia Cherepanova; Liam Fowl; Amin Ghiasi; Jonas Geiping; Micah Goldblum; Tom Goldstein; Arjun Gupta (ICASSP, 2021)

Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. - Jonas Geiping; Liam H. Fowl; W. Ronny Huang; Wojciech Czaja; Gavin Taylor; Michael Moeller 001; Tom Goldstein (ICLR, 2021)

Adversarial Examples Make Strong Poisons. - Liam Fowl; Micah Goldblum; Ping-yeh Chiang; Jonas Geiping; Wojciech Czaja; Tom Goldstein (NeurIPS, 2021)

What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors. - Jonas Geiping; Liam Fowl; Gowthami Somepalli; Micah Goldblum; Michael Moeller 001; Tom Goldstein (CoRR, 2021)

DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations. - Eitan Borgnia; Jonas Geiping; Valeriia Cherepanova; Liam Fowl; Arjun Gupta; Amin Ghiasi; Furong Huang; Micah Goldblum; Tom Goldstein (CoRR, 2021)

Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release. - Liam Fowl; Ping-yeh Chiang; Micah Goldblum; Jonas Geiping; Arpit Bansal; Wojtek Czaja; Tom Goldstein (CoRR, 2021)

Poisons that are learned faster are more effective. - Pedro Sandoval Segura; Vasu Singla; Liam Fowl; Jonas Geiping; Micah Goldblum; David Jacobs 001; Tom Goldstein (CVPR Workshops, 2022)

Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. - Gowthami Somepalli; Liam Fowl; Arpit Bansal; Ping-Yeh Chiang; Yehuda Dar; Richard G. Baraniuk; Micah Goldblum; Tom Goldstein (CVPR, 2022)

Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. - Liam H. Fowl; Jonas Geiping; Wojciech Czaja; Micah Goldblum; Tom Goldstein (ICLR, 2022)

Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. - Yuxin Wen; Jonas Geiping; Liam Fowl; Micah Goldblum; Tom Goldstein (ICML, 2022)

Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch. - Hossein Souri; Liam Fowl; Rama Chellappa; Micah Goldblum; Tom Goldstein (NeurIPS, 2022)

Execute Order 66: Targeted Data Poisoning for Reinforcement Learning. - Harrison Foley; Liam Fowl; Tom Goldstein; Gavin Taylor (CoRR, 2022)

Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning. - Yuxin Wen; Jonas Geiping; Liam Fowl; Hossein Souri; Rama Chellappa; Micah Goldblum; Tom Goldstein (CoRR, 2022)

Analysis of Data Security Vulnerabilities in Deep Learning. - Liam Fowl (2022)

Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting. - Beltrán Labrador; Guanlong Zhao; Ignacio López-Moreno; Angelo Scorza Scarpati; Liam Fowl; Quan Wang (ICASSP, 2023)

Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. - Hong-Min Chu; Jonas Geiping; Liam H. Fowl; Micah Goldblum; Tom Goldstein (ICLR, 2023)

Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. - Liam H. Fowl; Jonas Geiping; Steven Reich; Yuxin Wen; Wojciech Czaja; Micah Goldblum; Tom Goldstein (ICLR, 2023)

Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion. - Hossein Souri; Arpit Bansal; Hamid Kazemi; Liam Fowl; Aniruddha Saha; Jonas Geiping; Andrew Gordon Wilson; Rama Chellappa; Tom Goldstein; Micah Goldblum (CoRR, 2024)

3D Gaussian Splatting with Normal Information for Mesh Extraction and Improved Rendering. - Meenakshi Krishnan; Liam Fowl; Ramani Duraiswami (ICASSP Workshops, 2025)