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09/11/2023
Privacy Side Channels in Machine Learning Systems
Most current approaches for protecting privacy in machine learning (ML) ...
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09/09/2023
Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System
Neural language models are increasingly deployed into APIs and websites ...
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08/28/2023
Identifying and Mitigating the Security Risks of Generative AI
Every major technical invention resurfaces the dual-use dilemma – the ne...
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07/27/2023
Backdoor Attacks for In-Context Learning with Language Models
Because state-of-the-art language models are expensive to train, most pr...
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07/20/2023
A LLM Assisted Exploitation of AI-Guardian
Large language models (LLMs) are now highly capable at a diverse range o...
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06/26/2023
Are aligned neural networks adversarially aligned?
Large language models are now tuned to align with the goals of their cre...
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06/05/2023
Evading Black-box Classifiers Without Breaking Eggs
Decision-based evasion attacks repeatedly query a black-box classifier t...
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03/06/2023
Students Parrot Their Teachers: Membership Inference on Model Distillation
Model distillation is frequently proposed as a technique to reduce the p...
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02/27/2023
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators
It is becoming increasingly imperative to design robust ML defenses. How...
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02/20/2023
Poisoning Web-Scale Training Datasets is Practical
Deep learning models are often trained on distributed, webscale datasets...
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02/15/2023
Tight Auditing of Differentially Private Machine Learning
Auditing mechanisms for differential privacy use probabilistic means to ...
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02/02/2023
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data
“Effective robustness” measures the extra out-of-distribution (OOD) robu...
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01/30/2023
Extracting Training Data from Diffusion Models
Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion ha...
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12/28/2022
Publishing Efficient On-device Models Increases Adversarial Vulnerability
Recent increases in the computational demands of deep neural networks (D...
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12/13/2022
Considerations for Differentially Private Learning with Large-Scale Public Pretraining
The performance of differentially private machine learning can be booste...
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10/31/2022
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy
Studying data memorization in neural language models helps us understand...
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09/29/2022
No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"
New methods designed to preserve data privacy require careful scrutiny. ...
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06/28/2022
Increasing Confidence in Adversarial Robustness Evaluations
Hundreds of defenses have been proposed to make deep neural networks rob...
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06/21/2022
The Privacy Onion Effect: Memorization is Relative
Machine learning models trained on private datasets have been shown to l...
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03/31/2022
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets
We introduce a new class of attacks on machine learning models. We show ...
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02/15/2022
Quantifying Memorization Across Neural Language Models
Large language models (LMs) have been shown to memorize parts of their t...
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12/24/2021
Counterfactual Memorization in Neural Language Models
Modern neural language models widely used in tasks across NLP risk memor...
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09/28/2021
Unsolved Problems in ML Safety
Machine learning (ML) systems are rapidly increasing in size, are acquir...
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08/16/2021
NeuraCrypt is not private
NeuraCrypt (Yara et al. arXiv 2021) is an algorithm that converts a sens...
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07/14/2021
Deduplicating Training Data Makes Language Models Better
We find that existing language modeling datasets contain many near-dupli...
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06/28/2021
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent
Evading adversarial example detection defenses requires finding adversar...
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06/18/2021
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
Evaluating robustness of machine-learning models to adversarial examples...
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06/17/2021
Poisoning and Backdooring Contrastive Learning
Contrastive learning methods like CLIP train on noisy and uncurated trai...
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06/08/2021
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
We extend semi-supervised learning to the problem of domain adaptation t...
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06/08/2021
Handcrafted Backdoors in Deep Neural Networks
Deep neural networks (DNNs), while accurate, are expensive to train. Man...
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05/04/2021
Poisoning the Unlabeled Dataset of Semi-Supervised Learning
Semi-supervised machine learning models learn from a (small) set of labe...
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12/14/2020
Extracting Training Data from Large Language Models
It has become common to publish large (billion parameter) language model...
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09/30/2020
Erratum Concerning the Obfuscated Gradients Attack on Stochastic Activation Pruning
Stochastic Activation Pruning (SAP) (Dhillon et al., 2018) is a defense ...
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09/23/2020
A Partial Break of the Honeypots Defense to Catch Adversarial Attacks
A recent defense proposes to inject "honeypots" into neural networks in ...
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07/28/2020
Label-Only Membership Inference Attacks
Membership inference attacks are one of the simplest forms of privacy le...
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07/01/2020
Measuring Robustness to Natural Distribution Shifts in Image Classification
We study how robust current ImageNet models are to distribution shifts a...
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04/01/2020
Evading Deepfake-Image Detectors with White- and Black-Box Attacks
It is now possible to synthesize highly realistic images of people who d...
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03/10/2020
Cryptanalytic Extraction of Neural Network Models
We argue that the machine learning problem of model extraction is actual...
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02/19/2020
On Adaptive Attacks to Adversarial Example Defenses
Adaptive attacks have (rightfully) become the de facto standard for eval...
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02/11/2020
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Adversarial examples are malicious inputs crafted to induce misclassific...
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01/21/2020
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Semi-supervised learning (SSL) provides an effective means of leveraging...
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11/21/2019
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
We improve the recently-proposed "MixMatch" semi-supervised learning alg...
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10/29/2019
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications
We develop techniques to quantify the degree to which a given (training ...
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09/03/2019
High-Fidelity Extraction of Neural Network Models
Model extraction allows an adversary to steal a copy of a remotely deplo...
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07/12/2019
Stateful Detection of Black-Box Adversarial Attacks
The problem of adversarial examples, evasion attacks on machine learning...
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05/17/2019
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models
At IEEE S&P 2019, the paper "DeepSec: A Uniform Platform for Security An...
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05/06/2019
MixMatch: A Holistic Approach to Semi-Supervised Learning
Semi-supervised learning has proven to be a powerful paradigm for levera...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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03/25/2019
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness
Adversarial examples are malicious inputs crafted to cause a model to mi...
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03/22/2019
