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 ...
VentureBeat

Adversarial machine learning: The underrated threat of data poisoning

Most artificial intelligence researchers agree that one of the key concerns of machine learning is adversarial attacks, data manipulation techniques that cause trained models to behave in undesired ...
IT Business Edge

Adversarial Machine Learning: Combating Data Poisoning

The fields of machine learning (ML) and artificial intelligence (AI) have seen rapid developments in recent years. ML, a branch of AI and computer science, is the process through which computers can ...
The Next Web

What is adversarial machine learning?

To human observers, the following two images are identical. But researchers at Google showed in 2015 that a popular object detection algorithm classified the left image as “panda” and the right one as ...
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 ...
InfoWorld

Anti-adversarial machine learning defenses start to take root

Much of the anti-adversarial research has been on the potential for minute, largely undetectable alterations to images (researchers generally refer to these as “noise perturbations”) that cause AI’s ...
Business Journal Daily

AI Security Company Releases 2026 Threat Report

The report found that organizations are embedding AI deeper into critical operations while expanding their exposure to new ...
JD Supra

Adversarial Machine Learning in Focus: Novel Risks, Straightforward Legal Approaches

The Artificial Intelligence and Machine Learning (“AI/ML”) risk environment is in flux. One reason is that regulators are shifting from AI safety to AI innovation approaches, as a recent DataPhiles ...
University of Geneva

Defending against adversarial attacks by randomized diversification

The chain of the first 3 blocks can be organized in a parallel multi-channel structure that is followed by one or several aggregation blocks. The final decision about the class is made based on the ...
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 Attacks in Deep Learning for Wireless Communication

The integration of deep learning techniques into wireless communication systems has catalysed notable advancements in tasks such as modulation classification and spectrum sensing. However, the ...
The Next Web

Researchers have created a new technique to stop adversarial attacks

Did you know Neural is taking the stage this fall? Together with an amazing line-up of experts, we will explore the future of AI during TNW Conference 2021. Secure your ticket now! There’s growing ...