Weverify: Wider and Enhanced Verification for You Project Overview and Tools

Marinova, Zlatina; Spangenberg, Jochen; Teyssou, Denis; Papadopoulos, Symeon; Sarris, Nikos; Alaphilippe, Alexandre; Bontcheva, Kalina

doi:10.1109/icmew46912.2020.9106056

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…SocialTruth integrates its content verification with various platforms such as web search, journalist tools, content development, and a browser add-on [ 45 , 46 ]. WeVerify aims to address content verification challenges through participatory verification, open source algorithms, human-in-the-loop machine learning, and visualizations [ 47 , 48 ]. The project is designed for collaborative, decentralized content verification, tracking, and debunking [ 47 , 48 ] .…”

Section: Introductionmentioning

confidence: 99%

“…WeVerify aims to address content verification challenges through participatory verification, open source algorithms, human-in-the-loop machine learning, and visualizations [ 47 , 48 ]. The project is designed for collaborative, decentralized content verification, tracking, and debunking [ 47 , 48 ] . WeVerify has specific elements designed for journalists, such as tools for detecting the spread of misinformation.…”

Section: Introductionmentioning

confidence: 99%

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CoVerifi: A COVID-19 news verification system

Kolluri

Murthy

2021

Online Social Networks and Media

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CoVerifi: A COVID-19 news verification system

Kolluri

Murthy

2021

Online Social Networks and Media

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“…[5]. As a result, the EU have funded a range of FP7, H2020 and other projects to combat misinformation and disinformation including WeVerify [6,7], SocialTruth [8], PHEME [9,10], EUNOMIA [11] Fandango [12,13] and the European Digital Media Observatory (EDMO) [14]. Many other international organisations have also identified misinformation and disinformation as a threat and have increased efforts to combat it.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…Fake News Detector AI 7 claimed to use a neural network to detect similarity between submitted URLs and known fake news websites.…”

Section: No Longer Activementioning

confidence: 99%

PROVENANCE: An Intermediary-Free Solution for Digital Content Verification

Yousuf¹,

Qureshi²,

Spillane³

et al. 2021

Preprint

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The threat posed by misinformation and disinformation is one of the defining challenges of the 21 st century. Provenance is designed to help combat this threat by warning users when the content they are looking at may be misinformation or disinformation. It is also designed to improve media literacy among its users and ultimately reduce susceptibility to the threat among vulnerable groups within society. The Provenance browser plugin checks the content that users see on the Internet and social media and provides warnings in their browser or social media feed. Unlike similar plugins, which require human experts to provide evaluations and can only provide simple binary warnings, Provenance's state of the art technology does not require human input and it analyses seven aspects of the content users see and provides warnings where necessary.

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“…The work presented in this paper is conducted in the context of the WeVerify project [8], which aims to build an open-source platform that engages communities and citizen journalists alongside newsroom and freelance journalists for collaborative and decentralized content verification, tracking, and debunking. For the content verification task, we build on the existing Image Verification Assistant [9], which is a tool that analyses the authenticity of images by visualizing the outcomes of various tampering localization approaches.…”

Section: Introductionmentioning

confidence: 99%

Operation-wise Attention Network for Tampering Localization Fusion

Charitidis¹,

Kordopatis-Zilos²,

Papadopoulos³

et al. 2021

Preprint

Self Cite

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In this work, we present a deep learning-based approach for image tampering localization fusion. This approach is designed to combine the outcomes of multiple image forensics algorithms and provides a fused tampering localization map, which requires no expert knowledge and is easier to interpret by end users. Our fusion framework includes a set of five individual tampering localization methods for splicing localization on JPEG images. The proposed deep learning fusion model is an adapted architecture, initially proposed for the image restoration task, that performs multiple operations in parallel, weighted by an attention mechanism to enable the selection of proper operations depending on the input signals. This weighting process can be very beneficial for cases where the input signal is very diverse, as in our case where the output signals of multiple image forensics algorithms are combined. Evaluation in three publicly available forensics datasets demonstrates that the performance of the proposed approach is competitive, outperforming the individual forensics techniques as well as another recently proposed fusion framework in the majority of cases.

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Weverify: Wider and Enhanced Verification for You Project Overview and Tools

Cited by 10 publications

References 12 publications

CoVerifi: A COVID-19 news verification system

CoVerifi: A COVID-19 news verification system

PROVENANCE: An Intermediary-Free Solution for Digital Content Verification

Operation-wise Attention Network for Tampering Localization Fusion

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