{"id":254744,"date":"2021-11-30T17:08:00","date_gmt":"2021-11-30T09:08:00","guid":{"rendered":"https:\/\/www.um.edu.mo\/news-and-press-releases\/campus-news\/detail\/52938-2\/"},"modified":"2021-12-29T12:14:03","modified_gmt":"2021-12-29T04:14:03","slug":"52938","status":"publish","type":"post","link":"https:\/\/www.um.edu.mo\/pt-pt\/news-and-press-releases\/campus-news\/detail\/52938\/","title":{"rendered":"O detective IA de imagens falsificadas"},"content":{"rendered":"\n \n

It has never been easier to create forged images, which are now very convincing. At the University of Macau (UM), the team of Associate Professor Zhou Jiantao in the Department of Computer and Information Science has developed a cutting\u2011edge algorithm to detect such images. After defeating over 1,500 rivals at an international competition, they have continued to improve the algorithm under a project with the e\u2011commerce giant Alibaba. <\/p>

Seeing Is Not Believing <\/p>

Prof Zhou is the interim head of UM\u2019s Centre for Artificial Intelligence and Robotics, as well as a member of the university\u2019s State Key Laboratory of Internet of Things for Smart City. \u2018In many images, the forged parts are undetectable to the naked eye, sometimes even to computer programmes,\u2019 he says.  <\/p>

Like a human detective, a good image forgery detection tool has to recognise details that others might overlook. In digital images, such details include noise and other features. \u2018For instance, if an image has never been altered, there should be a consistent noise pattern throughout it,\u2019 Prof Zhou says.<\/p>

Since 2019, his team has developed new ways to accurately examine noise and extract features from images, in a project supported by the Macao Science and Technology Development Fund (FDCT). This project has inspired new algorithms for detecting forged images.<\/p>

The Truth in the Details<\/p>

In early 2021, Prof Zhou\u2019s team won the championship in the image forgery track, and the third prize in the detection track, at the Security AI Challenger Contest (Season 5). At this algorithm competition organised by Tsinghua University and Alibaba, the team altered 20 images, such as changing names and dates on ID cards, and added a special adaptive noise. \u2018We generated the noise after analysing background details and noise in the authentic parts, in order to hide the forgery traces from AI detection tools. We turned out to be the best forger among the 1,534 teams,\u2019 Prof Zhou says. <\/p>

For the same competition, Prof Zhou\u2019s 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\u2011network architecture that integrates spatial channel perception modules, which gives it an exceptional power for extracting features from images. <\/p>

A Business Partnership<\/p>

The team\u2019s performance has led to its collaboration with Alibaba to make the algorithm more robust. Every day, online marketplaces like Alibaba\u2019s 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\u2011resolution images, but they are not very effective with low\u2011quality, smaller images, which have usually already been compressed by messaging applications or social media platforms.<\/p>

Under the Alibaba Innovative Research Programme, the tech company has sponsored Prof Zhou\u2019s team to conduct a one\u2011year project titled \u2018Research on Highly Robust Methods for Detecting and Locating Forgery in Images Transmitted Through Media\u2019. 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.<\/p>

\u2018This is UM\u2019s first collaboration in this field with a big tech company,\u2019 Prof Zhou says, adding that his team has gained valuable experience in business\u2011university collaboration. \u2018We\u2019re applying our expertise to meet real\u2011world business challenges, and have seen encouraging progress.\u2019<\/p>

<\/p>

Source: UMagazine<\/a> Issue 24<\/p>

<\/p>

1. Unleashing the power of AI on self driving<\/a><\/p>

2. Smart marine robots to see greater use<\/a><\/p>

3. Intelligent robots serve society with innovations <\/a><\/p>

4. Social data drives smart tourism<\/a><\/p>

<\/p>\n \n ","protected":false},"excerpt":{"rendered":"

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