Liam Fowl | DeepAI

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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...

0 Beltrán Labrador, et al. ∙

∙ 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...

6 Yuxin Wen, et al. ∙

∙ 04/19/2022

Poisons that are learned faster are more effective

Imperceptible poisoning attacks on entire datasets have recently been to...

9 Pedro Sandoval Segura, et al. ∙

∙ 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...

0 Gowthami Somepalli, et al. ∙

∙ 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...

0 Yuxin Wen, et al. ∙

∙ 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 ...

0 Liam Fowl, et al. ∙

∙ 01/03/2022

Execute Order 66: Targeted Data Poisoning for Reinforcement Learning

Data poisoning for reinforcement learning has historically focused on ge...

0 Harrison Foley, et al. ∙

∙ 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...

0 Liam Fowl, et al. ∙

∙ 06/21/2021

Adversarial Examples Make Strong Poisons

The adversarial machine learning literature is largely partitioned into ...

7 Liam Fowl, et al. ∙

∙ 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...

3 Hossein Souri, et al. ∙

∙ 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...

0 Eitan Borgnia, et al. ∙

∙ 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...

0 Jonas Geiping, et al. ∙

∙ 02/16/2021

Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release

Large organizations such as social media companies continually release d...

0 Liam Fowl, et al. ∙

∙ 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...

0 Eitan Borgnia, et al. ∙

∙ 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...

2 Liam Fowl, et al. ∙

∙ 09/04/2020

Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching

Data Poisoning attacks involve an attacker modifying training data to ma...

18 Jonas Geiping, et al. ∙

∙ 04/20/2020

Headless Horseman: Adversarial Attacks on Transfer Learning Models

Transfer learning facilitates the training of task-specific classifiers ...

0 Ahmed Abdelkader, et al. ∙

∙ 04/01/2020

MetaPoison: Practical General-purpose Clean-label Data Poisoning

Data poisoning–the process by which an attacker takes control of a model...

0 W. Ronny Huang, et al. ∙

∙ 02/17/2020

Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks

Meta-learning algorithms produce feature extractors which achieve state-...

0 Micah Goldblum, et al. ∙

∙ 10/02/2019

Robust Few-Shot Learning with Adversarially Queried Meta-Learners

Previous work on adversarially robust neural networks requires large tra...

0 Micah Goldblum, et al. ∙

∙ 09/29/2019

Strong Baseline Defenses Against Clean-Label Poisoning Attacks

Targeted clean-label poisoning is a type of adversarial attack on machin...

0 Neal Gupta, et al. ∙

∙ 06/07/2019

Understanding Generalization through Visualizations

The power of neural networks lies in their ability to generalize to unse...

8 W. Ronny Huang, et al. ∙

∙ 05/23/2019

Adversarially Robust Distillation

Knowledge distillation is effective for producing small high-performance...

0 Micah Goldblum, et al. ∙

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