Second issue of the InVID newsletter
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This newsletter provides an update on the progress of the InVID project, which develops tools to help verify social media videos for news organizations. It summarizes the latest technologies developed, including story detection from Twitter streams, video fragmentation and annotation, near-duplicate video detection, logo detection, video context analysis, and video rights management. Prototypes of the Visual Analytics Dashboard, Verification Plugin, Verification Application, and Mobile Application are introduced. Recent dissemination activities promoting InVID and media coverage of the project are also outlined.
Second issue of the InVID newsletter
- 1. InVID - Verification of Social Media Video Content for the News Industry Inside this issue: - Welcome 1 - Progress Summary 1 - Latest InVID Technologies 2-3 - Latest InVID Prototypes 4-5 - InVID Dissemination Activities 6 - Public Feedback on the Verification Plugin 7 - InVID Consortium 8 We would like to welcome you to the second issue of the InVID Newsletter. The aim of this issue is to inform the community, our readers and supporters, about the developed tools and applications for media collection and verifi- cation. Initially, we outline the adopted software development methodology and the progress made so far. Subse- quently, we present our latest advances over a set of individual technologies for media collection, analysis, retrieval and rights management, and we introduce the developed InVID prototypes, namely, the Visual Analytics Dashboard, the Verification Plugin, the Verification Application and the Mobile Application. Following, we provide information about our recent dissemination activities, and the impact that these activities and the InVID prototypes have on the media and the news verification community. The current issue ends with details about the InVID consortium and how to get in contact with us. Progress Summary InVID project http://www.invid-project.eu November 2017Issue 2 The different steps of the agile methodology for the develop- ment and validation of the InVID platform and applications. Being fully aligned with the time-plan of the project, the InVID consortium has developed, tested and vali- dated a number of individual technologies and a set of integrated tools that address different aspects of the journalistic workflows for the verification of user- generated content. The development of each software component and the integration of these components into complete tools, was based on an agile develop- ment and innovation methodology which guides the entire work-plan. This methodology was chosen to en- sure quick development of technologies that meet the analysis requirements and fulfill the innovation goals of the project. According to this methodology, the members of the InVID consortium have worked on four successive de- velopment and validation cycles so far, which include (re-)prioritizing the industrial requirements; assessing, selecting and adapting individual technologies that best address these requirements; exposing and integrating them to the InVID platform and applications; pilot- testing and validating the updated platform and appli- cations; and repeating this cycle by re-prioritizing indus- try requirements in the light of the new status of plat- form and application development. The outcomes of this procedure include: A set of individual tools for media collection, analy- sis, indexing, retrieval and rights management; A number of integrated applications that address different parts of the media collection and verifica- tion process. Welcome!
- 2. Page 2 InVID - Verification of Social Media Video Content for the News Industry Latest InVID Technologies The InVID story detection component aims to effi- ciently and effectively find candidate videos for the journalistic verification process. The story detection algorithm was fine-tuned to extract news stories from real-time Twitter streams. The results can be ex- plored via a new "streamgraph" visualization (top figure). Stories are also used to dynamically direct a social media retrieval process, so that relevant videos are collected for the current news. The Story View on the Visual Analytics Dashboard (see p. 4) provides an intuitive means to browse the stories in the collected videos and explore the top videos for each story (bottom fig- ure). In this way, the story detection component supports journalists and other users in finding news-related social media video as a prior step to performing verification. Video Fragmentation & Annotation Story Detection Near-duplicate Video Detection of near-duplicates, where the top entry corresponds to the most similar near-duplicate and the last entry to the least similar one (see figure). Moreover, it inte- grates functionalities for the lo- calization of the query video in the retrieved near-duplicates, that allow to specify which part (s) were found to be similar to the query video. The service provides the needed functionalities for building, up- dating and querying the InVID index of video content via its REST API. This technology aims to find near- duplicates of a given video, aiming to assist journalists to detect and evalu- ate previous occurrence(s) of a news- worthy video. The retrieval of similar videos from the InVID index is performed by assessing their visual similarity with the query video, based on state-of-art deep learning methods. For a given video (currently supported platforms include YouTube, Dailymotion and Twitter) the underlying service returns a ranked list method was first improved by replacing the baseline DCNNs with two-sided DCNN archi- tecture that incorporates concept correla- tions and multi-task learning. Then, the above was again transformed to a more compact single-side architecture. The applied improvements increased the accuracy of the video annotation component (measured by Mean Average Precision-MAP) from 30.8% to 35.81% (left figure), while the needed processing time was reduced from 1.2 to 0.8 seconds per fragment (right fig- ure). The goal of this analysis module is to segment a user-generated- video into visually and temporally coherent fragments (called sub- shots), and to annotate these fragments with human-interpretable high-level concepts (such as building, car, daytime) that describe the visual content of each fragment. Using deep learning technolo- gies, the video annotation component predicts the concept labels for each video fragment in a fast and highly accurate manner. The method initially used in InVID combined the typical deep convolu- tional neural networks (DCNNs) with fine-tuning strategies. This
- 3. Page 3Issue 2 Latest InVID Technologies When a journalist comes across a Web video or image relating to a news story, being able to know its prove- nance could be critical in identifying potential biases or misrepresentations of the truth. Nowadays, besides well- known news agencies, various groups - from warring fac- tions to independent news organizations - use their own logos in the content they distribute. The InVID logo detec- tion component aims to detect any logos present in vid- eos or images and provide the name of the related or- ganization or channel. Using Deep Learning technologies, the component is already able to recognize the logos of more than 150 organizations, and to return the name of the organization plus a link to a related Wikipedia article. The component further allows users to submit additional logos for the system to learn. An online demonstration with examples of use can be found at: http://logos.iti.gr This component analyzes the online context of videos aiming to isolate and present to the user pieces of information that assist video's verification. The underlying service supports videos from the YouTube, Facebook and Twitter platforms. Extending the information collected by the first release of the tool (i.e. video/owner metadata, users’ comments), the latest ver- sion provides data about the weather conditions at the time and place where the video was supposedly captured (see figure on the right), thus, offering another means of video verification to the users. Moreover, it gathers tweets related to the evaluated video and presents them in a time- line-based view. Each of these tweets is analysed by the integrated machine learning algorithm, which offers a color-based (using a green or red bounding-box as shown in figure on the left) indication of whether the tweet is credible or not. The tool is publicly available at: http://caa.iti.gr This service aims to assist jour- nalists through the process of clearing the copyright of user- generated-content, in order to enable the reuse of this content. The module guides the journal- ists through recommended copy- right management workflows and supports videos made available through the YouTube, Twitter and Facebook platforms. In all cases, a summary of the de- fault reuse terms, as defined by each social network, is provided (see right image). If other kinds of reuse are necessary, the underlying service facilitates the generation of a custom reuse request (see left image) and allows to track the interactions between the journalists and the content owner, which include authorship confirmation and copyright agreements that define the terms for content reuse. The service can be tested at https://rights.invid.udl.cat and full access can be requested at invid@udl.cat Video Context Analysis Video Logo Detection Video Rights Management
- 4. Page 4 InVID - Verification of Social Media Video Content for the News Industry Latest InVID Prototypes InVID Visual Analytics Dashboard The InVID Verification Plugin is an integrated browser extension that can assist journalists and other media professionals on verifying selected newsworthy vid- eos. The tool allows the users to: Check for any prior use of a video by performing reverse video search on the Web using both You- Tube thumbnails and a rich set of InVID-extracted keyframes; Check contextual information about a video through mechanisms that support social-media- based contextual analysis, extraction of location, time and other video metadata, and keyframe/ image inspection though a digital magnifying glass; Check image forensics to get clues about potential tampering of the visual content, with the help of a set of integrated keyframe/image forensic filters; Find more related videos around a news story, via a user-friendly interface that enables advanced Twitter search using a time interval up to 1’. Screenshot of the InVID Visual Analytics Dashboard. The image magnifier tool of the InVID Verification Plugin. InVID Verification Plugin formation can be exported in PDF format using the automated report generation mechanism of the tool. The identified newsworthy videos are presented to the users via the embedded playback of the tool, either at the video-level or at a more fine-grained, video-fragment-level. Last but not least, a set of user-selected videos can be submitted for verification through the Verification Application of InVID. Accessible at: https://invid.weblyzard.com The InVID Visual Analytics Dashboard is an integrated tool for newsworthy video col- lection and management. It supports story detection across several social media channels (including Twitter, Youtube, DailyMotion and Vimeo). De- tected emerging stories are presented also in terms of their geographic distribution, allowing the users to gain more insights about the way information was transferred over the Web. Moreover, for each story the tool performs a story-based newsworthy video identification. The entire set of collected media undergoes an automatic metadata extraction and in- dexing process, and the available informa- tion (including both the collected media and the extracted metadata) is shown to the user through the context exploration and visualization functionalities of the user interface of the tool. Furthermore, this in- More than 1000 experts are currently using the tool for debunking fake news videos shared online! Get the tool for free from: http://www.invid-project.eu/verify
- 5. Page 5Issue 2 Latest InVID Prototypes InVID Verification Application InVID Mobile Application Example of using the near duplicate video detection com- ponent of the InVID Verification Application. Example of different interfaces of the InVID Mobile Application. Check additional contextual information about a video, assessing also historical weather data that can give more clues regarding the veracity of a video. The InVID Verification Application is an integrated tech- nology for advanced video verification, offering additional verification functionalities that go beyond those of the free Verification Plugin. Additionally to what the free browser extension supports, this tool enables the user to: Check any prior video use of a video by applying re- verse video search also in the InVID repository using advanced near-duplicate video detection methods, and to perform a careful inspection of the retrieved duplicates by parallel playback of query and duplicate video; Check the video’s origin and rights through integrated tools that perform video logo detection and rights management, allowing the user to assess the trust- worthiness of the video source and to get informed about the rights for re-use; Check video forensics to get insights about potential tampering of the video content, with the help of ad- vanced video forensic filters and frame-level video inspection in the player; The InVID Mobile Application is a native iOS and Android application which is used to provide trusted video contributions. This tool allows authorized users (such as the sub- scribers of a news agency or media organization) to capture videos of breaking or evolving stories, which are automatically enriched with time, location and device metadata. Moreover it makes possible the annotation of the captured videos using both free text and a set of pre-selected labels, enabling the users to provide more details about the recorded event. Finally, the captured trustworthy and meta- data-enriched videos can be submitted to the con- tent management systems of news agencies and media organizations, in order to be incorporated in their news flow. With the procedure described above, users who happen to be in the right place at the right time, can act as reporters, contributing to the time-efficient and precise coverage of a breaking news story.
- 6. Page 6 InVID - Verification of Social Media Video Content for the News Industry InVID Dissemination Activities InVID’s Presence in Industrial Events Media Coverage About InVID Published InVID Results Over the last year the InVID project and results were pro- moted, among others, in the following industrial events: - Big Data Value Association Valencia Summit, Valencia, Spain, December 1, 2016 - News Impact Summit, Amsterdam, the Netherlands, December 13, 2016 - EBU Verification Workshop, Lisbon, Portugal, January 23, 2017 - International Journalism Festival 2017, Perugia, Italy, April 7, 2017 - E-Day 2017, Vienna, Austria, April 12, 2017 - NAB Show 2017, Las Vegas, USA, April 24-27, 2017 - Futur en Seine 2017, Paris, France, June 8-10, 2017 - IFA 2017, Berlin, Germany, September 1-6, 2017 - WAN-IFRA Medialab Day Bordeaux, France, Septem- ber 8-9, 2017 - Brussels DisinfoLab, Brussels, Belgium, September 14, 2017 - IBC 2017, Amsterdam, the Netherlands, September 15 -19, 2017 - 49th FIBEP World Media Intelligence Congress, Berlin, Germany, October 4-6 , 2017 - Investors Meeting for Media Innovators, London, UK, October 5, 2017 - Digital Media World 2017, Berlin, Germany, October 10-12, 2017 - FujoMedia (Institute for Future Media and Journalism in Dublin) on July 5th, 2017 - Winbuzzer on July 7th, 2017 - The Next Web on July 8th, 2017 - Wired Italia on July 10th, 2017 - ITMedia Japan (Softbank Group) on July 10th, 2017 - Mediacentar Sarajevo on July 12th, 2017 - Focus Italia magazine on July 13th, 2017 - American Press Institute; The Week in Fact Checking on July 13th, 2017 - Mediashift.org on July 26th, 2017 - Media Sapiens Ukraine (taken from the above Medi- ashift) on July 26th, 2017 - StopFake.org Spanish on July 29th, 2017 - Parallaxi Mag on August 8th, 2017 - Mediakwest (Professional video and TV magazine) Full- page article on InVID on page 94; September-October 2017, #23 - WAN-IFRA blog World news publishing focus Report on InVID presentation in #DCXExpo Berlin, Oct. 12, 2017 The InVID project and its outputs were covered, among others, in the following channels: The InVID presentations / demonstrations at these events are available on the InVID SlideShare channel (https:// www.slideshare.net/InVID_EU) and the InVID website (http://www.invid-project.eu/presentations) Transactions on Multimedia) and 9 peer-reviewed international conference proceedings (IEEE Int. Conf. on Image Processing 2016, ACM Multimedia 2016, Int. Conf. on MultiMedia Modeling 2017, SocialNLP workshop at Euro- pean Chapter of the Association for Computational Linguistics 2017, ACM Int. Conf. on Multimedia Retrieval 2017, Int. Conf. on Computer Vision 2017, TRECVID 2017, Int. Conf. on Multimedia Modeling 2018). InVID also organized the 1st Int. Workshop on Multimedia Verification (MuVer) at the ACM Multimedia Conference 2017. The InVID publications and datasets are available at: - the InVID Community on Zenodo: https://zenodo.org/communities/invid-h2020 - the project website: http://www.invid-project.eu/publications & http://www.invid-project.eu/invid-datasets The InVID Verification Plugin was released as open source under an MIT License via the GitHub page of InVID: https://github.com/invideu/invid-verification-plugin Scientific results of InVID have been published or accepted for publication in 2 peer-reviewed international journals (Image & Vision Computing Journal, IEEE
- 7. Page 7Issue 2 Claire Wardle, First Draft News Research Director Mark Frankel, head of Social Media at BBC Dan Calderwood, head of multimedia of Times Group (SouthAfrica) MediaEU - EC account on media pluralism and freedom EAVI - European non-profit association on Media literacy Peter Burger, researcher and lecturer in Journalism & New Media at Leiden Univ. Sam Dubberley, manager @Amnesty's digital verification corps. Public Feedback on the Verification Plugin
- 8. Project Coordinator: Dr. Vasileios Mezaris Address: Centre for Research and Technology Hellas (CERTH) / Information Technologies Institute (ITI) 6th Km Charilaou-Thermi Road P.O. Box 60361, 57001 Thermi-Thessaloniki, Greece Tel: +30 2311 257770 Fax: +30 2310 474128 email: bmezaris@iti.gr web: http://www.iti.gr/~bmezaris Project and Contact Details Full title: “In Video Veritas – Verification of Social Media Video Content for the News Industry” Project identifier: H2020-687786 Start date: 1st January 2016 Duration: 36 months Funding agency: The InVID project has re- ceived funding from the European Union’s Horizon 2020 research and innovation pro- gramme under grant agreement No 687786. InVID Consortium Deutsche Welle http://www.dw.com Centre for Research & Technology Hel- las - Information Technologies Institute http://www.iti.gr Modul Technology http://www.modultech.eu Universitat de Lleida http://www.udl.cat Exo Makina http://www.exomakina.fr webLyzard Technology https://www.weblyzard.com Condat AG http://www.condat.de APA-IT Informations Technologie https://www.apa-it.at Agence France-Presse http://www.afp.com The list of the project partners with links to their official websites is given here. A more detailed presentation of the InVID partners, with a description of their expertise and roles in the project can be found on the project website (http://www.invid-project.eu/consortium). InVID - In Video Veritas! Verification of Social Media Video Content for the News Industry Find us online! Web: http://www.invid-project.eu Twitter: @InVID_EU https://twitter.com/InVID_EU LinkedIn: InVID Project https://www.linkedin.com/in/invid-project -1a712513b SlideShare: InVID Project http://www.slideshare.net/InVID_EU YouTube: InVID Project https://www.youtube.com/channel/ UCFp4OyFkV7cwQsDLCFRyBJQ Zenodo: InVID H2020 Project https://zenodo.org/communities/invid- h2020