Researchers at Concordia’s Gina Cody School of Engineering and Computer Science have developed a new approach to identifying fake news. And they say it will be able to find hidden patterns that reveal whether a particular item is likely fake or not.

The model, called SmoothDetector, integrates a probabilistic algorithm with a deep neural network. It’s designed to capture the uncertainties and key patterns in the shared latent representations of texts and images in a multimodal setting. The model uses annotated text and image data from the United States–based social media platform X and the China-based Weibo to learn. The researchers are currently looking into ways to eventually incorporate functionalities to detect fake audio and video content as well, leveraging every medium to counter misinformation.

Development of a new type of optical receiver, able to restore chaotic signals in free-space optical communication links distorted by atmospheric turbulence. By use of a system of optical antennas integrated into a programmable photonic chip, the receiver can adapt in real time, maintaining the integrity of the signal even in harsh atmospheric conditions. The study by a team of researchers led by Télécom Paris and the Politecnico di Milano, has just been published in Light: Science & Applications, and paves the way for the use of chaos-based encryption for secure, high-speed communication in hostile environments.