FMIBlog

AI Deception Tools Market to Reach USD 3.85 Billion by 2035 as AI-Driven Cyber Warfare and Autonomous Defense Systems Redefine Digital Security

The AI Deception Tools Market is witnessing rapid expansion as enterprises and governments increasingly adopt advanced ...
VentureBeat

Adversarial attacks in machine learning: What they are and how to stop them

Adversarial machine learning, a technique that attempts to fool models with deceptive data, is a growing threat in the AI and machine learning research community. The most common reason is to cause a ...
Infosecurity-magazine.com

#InfosecurityEurope: Preparing for Adversarial Machine Learning Attacks

Machine learning (ML) and artificial intelligence (AI) are essential components in modern and effective cybersecurity solutions. However, as the use of ML and AI in cybersecurity is increasingly ...
HHS

AI Deception Is a Story, Not Just Code

This shift needs a radically new approach, one rooted in cognitive defense, not just technical controls, said Perry Carpenter, chief human risk management strategist at KnowBe4. "The battlefield is of ...
Forbes

AI Ethics Tempted But Hesitant To Use AI Adversarial Attacks Against The Evils Of Machine Learning, Including For Self-Driving Cars

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. It is widely accepted sage wisdom to garner as much as you can ...
AFCEA

Adversarial Machine Learning Poses a New Threat to National Security

Imagine the following scenarios: An explosive device, an enemy fighter jet and a group of rebels are misidentified as a cardboard box, an eagle or a sheep herd. A lethal autonomous weapons system ...
The National Law Review

NIST Finalizes Cyber Attack Guidance for Adversarial Machine Learning

We collaborate with the world's leading lawyers to deliver news tailored for you. Sign Up for any (or all) of our 25+ Newsletters. Some states have laws and ethical rules regarding solicitation and ...
Nature

Adversarial Machine Learning

Adversarial machine learning studies the creation and defence against inputs—known as adversarial examples—that are intentionally perturbed to mislead trained models. Deep networks and other ...
Nature

Towards quantum enhanced adversarial robustness in machine learning

Machine learning algorithms are powerful tools for data-driven tasks such as image classification and feature detection. However, their vulnerability to adversarial examples—input samples manipulated ...
The Hill

Money, markets and machine learning: Unpacking the risks of adversarial AI

It is impossible to ignore the critical role that artificial intelligence (AI) and its subset, machine learning, play in the stock market today. While AI refers to machines that can perform tasks that ...
Hosted on MSN

Machine learning methods are best suited to catch liars, according to science of deception detection

Scientists have revealed that Convolutional Neural Networks (CNNs), a type of deep learning algorithm, demonstrate superior performance compared to conventional non-machine learning approaches when ...