UMagazine_24

澳大新語 • 2021 UMAGAZINE 24 23 封面專題 • COVER STORY Under the Alibaba Innovative Research Programme, the tech company has sponsored Prof Zhou’s team to conduct a one‑year project titled ‘Research on Highly Robust Methods for Detecting and Locating Forgery in Images Transmitted Through Media’. It aims to develop an algorithm that can accurately detect forged parts of images even if they have been compressed by different media, resized, filtered, or contain added noise. ‘This is UM’s first collaboration in this field with a big tech company,’ Prof Zhou says, adding that his team has gained valuable experience in business‑university collaboration. ‘We’re applying our expertise to meet real‑world business challenges, and have seen encouraging progress.’ For the same competition, Prof Zhou’s team also trained an algorithm that, after learning from tens of thousands of images, can detect forged areas in less than half a second. It outperformed most of its competitors largely due to a multi‑network architecture that integrates spatial channel perception modules, which gives it an exceptional power for extracting features from images. A Business Partnership The team’s performance has led to its collaboration with Alibaba to make the algorithm more robust. Every day, online marketplaces like Alibaba’s need to verify countless business licenses to make sure they are dealing with legitimate sellers. Existing algorithms perform reasonably well in detecting forgery in high‑resolution images, but they are not very effective with low‑quality, smaller images, which have usually already been compressed by messaging applications or social media platforms. 周建濤教授團隊的算法可在半秒內偵測出圖像被篡改的位置 Prof Zhou Jiantaoʼs team has developed an algorithm that can detect forged areas in an image in less than half a second 周建濤教授團隊運用澳大的智能超算中心來訓練算法。該 中心提供多個GPU計算平台,可以執行深度學習任務和作 為虛擬數據中心 。 Prof Zhou Jiantaoʼs team uses the Super Intelligent Computing Centre at UM to train their algorithms. The centre hosts GPU computing platforms which can run deep learning tasks and serve as virtual data centres.

RkJQdWJsaXNoZXIy MTQ1NDU2Ng==