207 citations · 1.1k across the 49 of their papers we have counts for
8 papers · 1 filter
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models
Liam Fowl, Jonas Geiping, Wojtek Czaja +2
Federated learning has quickly gained popularity with its promises of increased user privacy and efficiency. Previous works have shown that federated gradient updates contain infor…
Stochastic Training is Not Necessary for Generalization
Jonas Geiping, Micah Goldblum, Phillip E. Pope +2
It is widely believed that the implicit regularization of SGD is fundamental to the impressive generalization behavior we observe in neural networks. In this work, we demonstrate t…
Is Differentiable Architecture Search truly a One-Shot Method?
Jonas Geiping, Jovita Lukasik, Margret Keuper +1
Differentiable architecture search (DAS) is a widely researched tool for the discovery of novel architectures, due to its promising results for image classification. The main benef…
Adversarial Examples Make Strong Poisons
Liam Fowl, Micah Goldblum, Ping-yeh Chiang +3
The adversarial machine learning literature is largely partitioned into evasion attacks on testing data and poisoning attacks on training data. In this work, we show that adversari…
Training or Architecture? How to Incorporate Invariance in Neural Networks
Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner +2
Many applications require the robustness, or ideally the invariance, of a neural network to certain transformations of input data. Most commonly, this requirement is addressed by e…
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations
Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova +6
Data poisoning and backdoor attacks manipulate training data to induce security breaches in a victim model. These attacks can be provably deflected using differentially private (DP…
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release
Liam Fowl, Ping-yeh Chiang, Micah Goldblum +4
Large organizations such as social media companies continually release data, for example user images. At the same time, these organizations leverage their massive corpora of releas…
What Doesn't Kill You Makes You Robust(er): How to Adversarially Train against Data Poisoning
Jonas Geiping, Liam Fowl, Gowthami Somepalli +3
Data poisoning is a threat model in which a malicious actor tampers with training data to manipulate outcomes at inference time. A variety of defenses against this threat model hav…