2.3k citations · 7.1k across the 22 of their papers we have counts for
24 papers
Certified Robustness to Clean-Label Poisoning Using Diffusion Denoising
Sanghyun Hong, Nicholas Carlini, Alexey Kurakin
We present a certified defense to clean-label poisoning attacks under -norm. These attacks work by injecting a small number of poisoning samples (e.g., 1%) that contain bou…
Harnessing large-language models to generate private synthetic text
Alexey Kurakin, Natalia Ponomareva, Umar Syed +2
Differentially private training algorithms like DP-SGD protect sensitive training data by ensuring that trained models do not reveal private information. An alternative approach, w…
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin +6
ML models are ubiquitous in real world applications and are a constant focus of research. At the same time, the community has started to realize the importance of protecting the pr…
RETVec: Resilient and Efficient Text Vectorizer
Elie Bursztein, Marina Zhang, Owen Vallis +2
This paper describes RETVec, an efficient, resilient, and multilingual text vectorizer designed for neural-based text processing. RETVec combines a novel character encoding with an…
Publishing Efficient On-device Models Increases Adversarial Vulnerability
Sanghyun Hong, Nicholas Carlini, Alexey Kurakin
Recent increases in the computational demands of deep neural networks (DNNs) have sparked interest in efficient deep learning mechanisms, e.g., quantization or pruning. These mecha…
Differentially Private Image Classification from Features
Harsh Mehta, Walid Krichene, Abhradeep Thakurta +2
Leveraging transfer learning has recently been shown to be an effective strategy for training large models with Differential Privacy (DP). Moreover, somewhat surprisingly, recent w…
Large Scale Transfer Learning for Differentially Private Image Classification
Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin +1
Differential Privacy (DP) provides a formal framework for training machine learning models with individual example level privacy. In the field of deep learning, Differentially Priv…
Toward Training at ImageNet Scale with Differential Privacy
Alexey Kurakin, Shuang Song, Steve Chien +3
Differential privacy (DP) is the de facto standard for training machine learning (ML) models, including neural networks, while ensuring the privacy of individual examples in the tr…
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
David Berthelot, Rebecca Roelofs, Kihyuk Sohn +2
We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one…
Handcrafted Backdoors in Deep Neural Networks
Sanghyun Hong, Nicholas Carlini, Alexey Kurakin
When machine learning training is outsourced to third parties, become practical as the third party who trains the model may act maliciously to inject hidden be…
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin +8
Convex relaxations have emerged as a promising approach for verifying desirable properties of neural networks like robustness to adversarial perturbations. Widely used Linear Progr…
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li +6
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance. In this paper, we demonstrate the power of a simple combin…
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
David Berthelot, Nicholas Carlini, Ekin D. Cubuk +4
We improve the recently-proposed "MixMatch" semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring. Distribution al…
High Accuracy and High Fidelity Extraction of Neural Networks
Matthew Jagielski, Nicholas Carlini, David Berthelot +2
In a model extraction attack, an adversary steals a copy of a remotely deployed machine learning model, given oracle prediction access. We taxonomize model extraction attacks aroun…
On Evaluating Adversarial Robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot +6
Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that wi…
Adversarial Vision Challenge
Wieland Brendel, Jonas Rauber, Alexey Kurakin +5
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks…
Adversarial Attacks and Defences Competition
Alexey Kurakin, Ian Goodfellow, Samy Bengio +20
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop…
Adversarial Logit Pairing
Harini Kannan, Alexey Kurakin, Ian Goodfellow
In this paper, we develop improved techniques for defending against adversarial examples at scale. First, we implement the state of the art version of adversarial training at unpre…
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung +4
Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ost…
Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot +3
Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale t…
Large-Scale Evolution of Image Classifiers
Esteban Real, Sherry Moore, Andrew Selle +5
Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Our goal is…
Adversarial Machine Learning at Scale
Alexey Kurakin, Ian Goodfellow, Samy Bengio
Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks wit…
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
Nicolas Papernot, Fartash Faghri, Nicholas Carlini +23
CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be us…
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, Samy Bengio
Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in…