In response to the growing threat of deepfake technology, we introduce BENet, a Cross-Domain Robust Bias Expansion Network. BENet enhances the detection of fake faces by addressing limitations in current detectors related to variations across different types of fake face generation techniques, where ``cross-domain" refers to the diverse range of these deepfakes, each considered a separate domain. BENet's core feature is a bias expansion module based on autoencoders. This module maintains genuine facial features while enhancing differences in fake reconstructions, creating a reliable bias for detecting fake faces across various deepfake domains. We also introduce a Latent-Space Attention (LSA) module to capture inconsistencies related to fake faces at different scales, ensuring robust defense against advanced deepfake techniques. The enriched LSA feature maps are multiplied with the expanded bias to create a versatile feature space optimized for subtle forgeries detection. To improve its ability to detect fake faces from unknown sources, BENet integrates a cross-domain detector module that enhances recognition accuracy by verifying the facial domain during inference. We train our network end-to-end with a novel bias expansion loss, adopted for the first time, in face forgery detection. Extensive experiments covering both intra and cross-dataset demonstrate BENet's superiority over current state-of-the-art solutions.

A novel material made from rotting fruit and vegetables that absorbs stray UV light from the sun and converts it into renewable energy has landed its designer the first sustainability gong in this year's James Dyson awards. From a record 1,800 entries – despite the challenges of Covid-19 – the award was given to 27-year-old Carvey Ehren Maigue, a student at Mapúa University in the Philippines, for his Aureus system which uses the natural scientific principles behind the northern lights. The other top prize in the international competition has been handed to the inventor of a low-cost biomedical device that can be used at home to detect breast cancer, harnessing artificial intelligence to analyse urine. Aureus is made from crop waste and can be attached in panels to windows and walls. It allows high energy particles derived from fruit and vegetables to be absorbed by luminescent particles, which re-emit them as visible light.