Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020...

a single adversarially chosen pixel. Machine learning models are often vulnerable to manipulation or evasion via adversarial machine learning. Researchers...

A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence...

Deep learning is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression...

in the fields of computer security and machine learning. He is known for his work on adversarial machine learning, particularly his work on the Carlini...

develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize...

The privacy risk is expected to grow as machine learning techniques and text corpora develop. All adversarial stylometry shares the core idea of faithfully...

authority of a claim without rigorously investigating its source. In adversarial machine learning, information laundering refers to a general strategy that purposely...

instances where non-existent objects are erroneously detected because of adversarial attacks. The term "hallucinations" in AI gained wider recognition during...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence within computer...

Weighing Algorithm for Adversarial Utility-based Dueling Bandits" (PDF), Proceedings of the 32nd International Conference on Machine Learning (ICML-15), archived...

Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning...

Domain adaptation is a field associated with machine learning and transfer learning. It addresses the challenge of training a model on one data distribution...

Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning...

generative adversarial network (GAN). Goodfellow co-wrote, as the first author, the textbook Deep Learning (2016) and wrote the chapter on deep learning in the...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

Generative Adversarial Network (WGAN) is a variant of generative adversarial network (GAN) proposed in 2017 that aims to "improve the stability of learning, get...

Machine learning techniques used for content generation include Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN), Generative Adversarial...

recognition technology make dazzle makeup increasingly ineffective. Adversarial machine learning Valenti, Lauren (March 30, 2018). "Yes, There's a Way to Outsmart...

Applying machine learning (ML) (including deep learning) methods to the study of quantum systems is an emergent area of physics research. A basic example...

In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization...

A large language model (LLM) is a machine learning model designed for natural language processing tasks, especially language generation. LLMs are language...

by applying unsupervised machine learning (invoking for instance neural network architectures such as generative adversarial networks (GANs), variation...

The methods that Fawkes uses can be identified as similar to adversarial machine learning. This method trains a facial recognition software using already...

for one year. Microsoft and MITRE partnered on the open source Adversarial Machine Learning Threat Matrix in collaboration with IBM, Nvidia, and academic...

the top seven finalists. Her most recent work is understanding adversarial machine learning, and blockchains. Song is the founder of Oasis Labs. At UC Berkeley...

In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update...

might try to manipulate their outcome in own favor and even use adversarial machine learning. According to Harari, the conflict between democracy and dictatorship...

pose security risks in machine learning systems where attackers have access to the training data (See adversarial machine learning). Koh and Liang’s contributions...