b'Jonas Geiping'

research

∙ 09/01/2023

Baseline Defenses for Adversarial Attacks Against Aligned Language Models

As Large Language Models quickly become ubiquitous, it becomes critical ...

0 Neel Jain, et al. ∙

research

∙ 07/09/2023

Augmenters at SemEval-2023 Task 1: Enhancing CLIP in Handling Compositionality and Ambiguity for Zero-Shot Visual WSD through Prompt Augmentation and Text-To-Image Diffusion

This paper describes our zero-shot approaches for the Visual Word Sense ...

0 Jie S. Li, et al. ∙

research

∙ 06/29/2023

Seeing in Words: Learning to Classify through Language Bottlenecks

Neural networks for computer vision extract uninterpretable features des...

0 Khalid Saifullah, et al. ∙

research

∙ 06/28/2023

On the Exploitability of Instruction Tuning

Instruction tuning is an effective technique to align large language mod...

0 Manli Shu, et al. ∙

research

∙ 06/23/2023

Bring Your Own Data! Self-Supervised Evaluation for Large Language Models

With the rise of Large Language Models (LLMs) and their ubiquitous deplo...

1 Neel Jain, et al. ∙

research

∙ 06/07/2023

On the Reliability of Watermarks for Large Language Models

As LLMs become commonplace, machine-generated text has the potential to ...

5 John Kirchenbauer, et al. ∙

research

∙ 05/31/2023

Understanding and Mitigating Copying in Diffusion Models

Images generated by diffusion models like Stable Diffusion are increasin...

0 Gowthami Somepalli, et al. ∙

research

∙ 05/31/2023

Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust

Watermarking the outputs of generative models is a crucial technique for...

0 Yuxin Wen, et al. ∙

research

∙ 05/30/2023

What Can We Learn from Unlearnable Datasets?

In an era of widespread web scraping, unlearnable dataset methods have t...

0 Pedro Sandoval Segura, et al. ∙

research

∙ 04/24/2023

A Cookbook of Self-Supervised Learning

Self-supervised learning, dubbed the dark matter of intelligence, is a p...

0 Randall Balestriero, et al. ∙

research

∙ 04/05/2023

JPEG Compressed Images Can Bypass Protections Against AI Editing

Recently developed text-to-image diffusion models make it easy to edit o...

0 Pedro Sandoval Segura, et al. ∙

research

∙ 02/14/2023

Universal Guidance for Diffusion Models

Typical diffusion models are trained to accept a particular form of cond...

0 Arpit Bansal, et al. ∙

research

∙ 02/07/2023

Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery

The strength of modern generative models lies in their ability to be con...

12 Yuxin Wen, et al. ∙

research

∙ 01/24/2023

A Watermark for Large Language Models

Potential harms of large language models can be mitigated by watermarkin...

3 John Kirchenbauer, et al. ∙

research

∙ 12/28/2022

Cramming: Training a Language Model on a Single GPU in One Day

Recent trends in language modeling have focused on increasing performanc...

0 Jonas Geiping, et al. ∙

research

∙ 12/07/2022

Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models

Cutting-edge diffusion models produce images with high quality and custo...

0 Gowthami Somepalli, et al. ∙

research

∙ 10/23/2022

K-SAM: Sharpness-Aware Minimization at the Speed of SGD

Sharpness-Aware Minimization (SAM) has recently emerged as a robust tech...

0 Renkun Ni, et al. ∙

research

∙ 10/19/2022

Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries

As industrial applications are increasingly automated by machine learnin...

0 Yuxin Wen, et al. ∙

research

∙ 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. ∙

research

∙ 10/12/2022

How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization

Despite the clear performance benefits of data augmentations, little is ...

0 Jonas Geiping, et al. ∙

research

∙ 09/24/2022

A Simple Strategy to Provable Invariance via Orbit Mapping

Many applications require robustness, or ideally invariance, of neural n...

0 Kanchana Vaishnavi Gandikota, et al. ∙

research

∙ 08/19/2022

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise

Standard diffusion models involve an image transform – adding Gaussian n...

12 Arpit Bansal, et al. ∙

research

∙ 06/08/2022

Autoregressive Perturbations for Data Poisoning

The prevalence of data scraping from social media as a means to obtain d...

0 Pedro Sandoval Segura, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 09/29/2021

Stochastic Training is Not Necessary for Generalization

It is widely believed that the implicit regularization of stochastic gra...

0 Jonas Geiping, et al. ∙

research

∙ 08/12/2021

DARTS for Inverse Problems: a Study on Hyperparameter Sensitivity

Differentiable architecture search (DARTS) is a widely researched tool f...

0 Jonas Geiping, et al. ∙

research

∙ 06/21/2021

Adversarial Examples Make Strong Poisons

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

7 Liam Fowl, et al. ∙

research

∙ 06/18/2021

Training or Architecture? How to Incorporate Invariance in Neural Networks

Many applications require the robustness, or ideally the invariance, of ...

0 Kanchana Vaishnavi Gandikota, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 04/23/2020

Fast Convex Relaxations using Graph Discretizations

Matching and partitioning problems are fundamentals of computer vision a...

1 Jonas Geiping, et al. ∙

research

∙ 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. ∙

research

∙ 03/31/2020

Inverting Gradients – How easy is it to break privacy in federated learning?

The idea of federated learning is to collaboratively train a neural netw...

0 Jonas Geiping, et al. ∙

research

∙ 11/18/2019

WITCHcraft: Efficient PGD attacks with random step size

State-of-the-art adversarial attacks on neural networks use expensive it...

0 Ping-Yeh Chiang, et al. ∙

research

∙ 10/01/2019

Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory

We empirically evaluate common assumptions about neural networks that ar...

0 Micah Goldblum, et al. ∙

research

∙ 08/17/2019

Parametric Majorization for Data-Driven Energy Minimization Methods

Energy minimization methods are a classical tool in a multitude of compu...

6 Jonas Geiping, et al. ∙

research

∙ 02/20/2018

Composite Optimization by Nonconvex Majorization-Minimization

Many tasks in imaging can be modeled via the minimization of a nonconvex...

0 Jonas Geiping, et al. ∙

research

∙ 11/23/2016

Multiframe Motion Coupling for Video Super Resolution

The idea of video super resolution is to use different view points of a ...

0 Jonas Geiping, et al. ∙

Jonas Geiping

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