Fake Detection

How can you tell when you’re reading machine-generated text? Three recent papers proposed solutions: Watermarking, classification, and a statistical method.

Robocalls slip through smartphone spam filters, but a new generation of deep learning tools promises to tighten the net. Research proposed fresh approaches to thwarting robocalls. Such innovations soon could be deployed in apps.

DeepMind released three papers that push the boundaries — and examine the issues — of large language models.

Accusations of fraud hang over some of the world’s most highly valued artworks. Machine learning engineers are evaluating the authenticity of these famous pieces.

A supergroup of machine learning models flags manipulated photos. Jigsaw, a tech incubator owned by Alphabet, released a system that detects digitally altered images.

Deepfakes have gone mainstream, allowing celebrities to star in commercials without setting foot in a film studio. A new method helps determine whether such endorsements — and other images produced by generative adversarial networks — are authentic.

Facing a tsunami of user-generated disinformation, YouTube is scrambling to stop its recommendation algorithm from promoting videos that spread potentially dangerous falsehoods.

A machine learning model is scouring the cosmos for undiscovered planets. Astronomers from the University of Warwick developed a system that learned to identify faraway worlds in a dataset of thousands of candidates.

A neural network is helping credit card users continue to shop even when the lender’s credit-approval network goes down. Visa developed a deep learning system that analyzes individual cardholders’ behavior in real time to predict whether credit card transactions should be approved or denied.

The pandemic has radically altered online shopping behavior, throwing a wrench into many AI systems. AI inventory trackers, recommendation algorithms, and fraud detection systems trained on pre-pandemic consumer behavior have been flummoxed by the different ways people now browse, binge, and buy.

Facebook’s AI can’t spot Covid-19 disinformation on its own. But with human help, it can slow the spread. Facebook uses a combination of humans and neural nets to crack down on messages that make false claims about Covid-19, which may have deadly consequences.

AI’s ability to produce synthetic pictures that fool humans into believing they’re real has spurred a race to build neural networks that can tell the difference. Recent research achieved encouraging results.

AI’s ability to transfer a person’s face from a source photo onto someone in a target photo doesn’t work so well when the target face is partially obscured by, say, eyeglasses, a veil, or a hand. A new technique handles such occlusions.

Facebook announced a ban on deepfake videos, on the heels of a crackdown on counterfeit profiles that used AI-generated faces. Facebook declared this week that it will remove deepfake videos it deems deliberately misleading.

Ignorance is a choice in the Internet age. Virtually all of human knowledge is available for the cost of typing a few words into a search box.