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11/11/2022
Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting
In this paper, we present a novel approach to adapt a sequence-to-sequen...
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10/17/2022
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning
Federated learning is particularly susceptible to model poisoning and ba...
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04/19/2022
Poisons that are learned faster are more effective
Imperceptible poisoning attacks on entire datasets have recently been to...
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03/15/2022
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective
We discuss methods for visualizing neural network decision boundaries an...
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02/01/2022
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification
Federated learning (FL) has rapidly risen in popularity due to its promi...
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01/29/2022
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
A central tenet of Federated learning (FL), which trains models without ...
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01/03/2022
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning
Data poisoning for reinforcement learning has historically focused on ge...
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10/25/2021
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models
Federated learning has quickly gained popularity with its promises of in...
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06/21/2021
Adversarial Examples Make Strong Poisons
The adversarial machine learning literature is largely partitioned into ...
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06/16/2021
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch
As the curation of data for machine learning becomes increasingly automa...
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03/02/2021
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations
Data poisoning and backdoor attacks manipulate training data to induce s...
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02/26/2021
What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors
Data poisoning is a threat model in which a malicious actor tampers with...
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02/16/2021
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release
Large organizations such as social media companies continually release d...
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11/18/2020
Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff
Data poisoning and backdoor attacks manipulate victim models by maliciou...
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10/13/2020
Random Network Distillation as a Diversity Metric for Both Image and Text Generation
Generative models are increasingly able to produce remarkably high quali...
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09/04/2020
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching
Data Poisoning attacks involve an attacker modifying training data to ma...
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04/20/2020
Headless Horseman: Adversarial Attacks on Transfer Learning Models
Transfer learning facilitates the training of task-specific classifiers ...
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04/01/2020
MetaPoison: Practical General-purpose Clean-label Data Poisoning
Data poisoning–the process by which an attacker takes control of a model...
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02/17/2020
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
Meta-learning algorithms produce feature extractors which achieve state-...
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10/02/2019
Robust Few-Shot Learning with Adversarially Queried Meta-Learners
Previous work on adversarially robust neural networks requires large tra...
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09/29/2019
Strong Baseline Defenses Against Clean-Label Poisoning Attacks
Targeted clean-label poisoning is a type of adversarial attack on machin...
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06/07/2019
Understanding Generalization through Visualizations
The power of neural networks lies in their ability to generalize to unse...
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05/23/2019
