Fine-tuning the neural network on many adversarial examples will make it more robust against adversarial attacks. Adversarial training results in a slight drop in the accuracy of a deep learning ...

Adversarial examples do not just apply to neural networks that process visual data. There is also research on adversarial machine learning on text and audio data.

It’s true that the AI community lacks any clear consensus on best practices for building anti-adversarial defenses into deep neural networks. But from what I see in the research literature and ...

We’ve touched previously on the concept of adversarial examples—the class of tiny changes that, when fed into a deep-learning model, cause it to misbehave. In March, we covered UC Berkeley ...

Description Generative adversarial networks, or GANs, are deep learning frameworks for unsupervised learning that utilize two neural networks. The two networks are pitted against each other, with one ...

Example of a stop sign that is hacked to be identified as a speed limit sign by a neural network. Machine learning ranks images with a percent confidence to create a classifier group.

Let's explore the potential adversarial attacks on AI systems, the security challenges they pose and solutions on how to navigate this landscape and keep models secure.

New research from MIT is shedding light on the mysterious inner workings of Generative Adversarial Networks (GANs), and can reveal how the algorithms make chillingly human-like decisions about the ...

READ MORE: A Style-Based Generator Architecture for Generative Adversarial Networks [arXiv] More on neural network-generated faces: These People Never Existed. They Were Made by an AI.