Veracity Ai Veri Fact Ai

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Learn more about arXivLabs. Veracity AI at the Florida State University College of Communication and Information, founded by Dr. Shuyuan Metcalfe, is committed to using artificial intelligence to automate the detection of manipulation in photographic images. The mission is to combat disinformation from human-made manipulated images, which can be easily perceived as truth. Our research assists in the fight for truth by efficiently identifying fabrication in real world visual media with accuracy. The prediction architecture uses a deep consensus algorithm running on an on-site set of high-performance computing nodes.

The deep consensus model generates a prediction mask, indicating where manipulations have occurred in an image on a pixel level. The model is trained in three manipulation categories: splicing, inpainting, and copy-move. Splicing Splicing is the process of inserting a region of an image into another image. In the example below, the red and white plane is spliced into the image. Inpainting Inpainting is the process of removing a region of an image. In the example below, a bus on the left is removed using inpainting on the image.

Copy-move Copy-move is the process of copying a region of an image and pasting it into another region of the image. In the image below, the hotdog with mustard is copied and moved to the top of the image. Large Language Models (LLMs) are a powerful kind of instruction-ready AI chatbot. They are good at summarizing text like subject-matter experts would. Unlike experts, they can sometimes get basic things wrong in any number of ways. That's why asking them to reason on their own about how likely it is that a statement is true or false is not a reliable approach to using LLMs for fact-checking.

Veri-fact.ai takes a more reliable approach based on “Retrieval-Augmented Generation” or RAG for short. Here, this means having the LLM base its response on relevant text from reliable sources on the internet. Veri-fact.ai does this by: There are a few minor additional steps to this process, e.g.: That's all there is to it. Now you know!

See our User Guidelines for more details on how to use the app. An academic paper of an earlier version of the approach that goes into more technical detail is: The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications.

This paper will showcase Veracity’s ability to not only detect misinformation but also explain its reasoning, fostering media literacy and promoting a more informed society. Experts have rated the dissemination of misinformation and disinformation as the #1 risk the world faces Torkington (2024). This risk has only increased with the proliferation and advancement of generative AI Bowen et al. (2024); Pelrine et al. (2023b). Responses to misinformation have up to now been largely centred around platform moderation.

As large-scale social media platforms actively eliminate their content moderation teams Horvath et al. (2025), they pass to the user the personal and social responsibility to assess the reliability of claims and figure out how to make well-grounded decisions in a landscape of uncertain information. In the absence of strong platform-based approaches, solutions that support and empower individuals with tools to validate the information they encounter become essential in dampening the societally corrosive effects of misinformation. Misinformation is particularly dangerous when it influences public health and democratic processes, as seen in the spread of vaccine-related disinformation and politically motivated claims about censorship, both of which have been shown to exacerbate... With the rollback of content moderation efforts and increasing concerns over algorithmic bias on social media platforms, independent, reliable fact-checking tools are more necessary than ever. A promising solution in this area is an AI Steward that helps people fact-check and filter out manipulative and fake information.

In fact, AI can outperform human fact-checkers in both accuracy Wei et al. (2024); Zhou et al. (2024) and helpfulness Zhou et al. (2024). Although there is rapid progress in improving the accuracy of such systems Tian et al. (2024); Wei et al.

(2024); Ram et al. (2024), there is much less research on how to make a high-accuracy system into a helpful and trustworthy one that users can rely on Augenstein et al. (2024). Our AI-powered open-source solution, Veracity, deploys large language models (LLMs) working with web retrieval agents to provide any member of the public with an efficient and grounded analysis of how factual their input text... Moreover, through open-sourcing our platform, we hope to bring a test-bed for the research community to design effective fact-checking strategies. Thanks for trying out veri-fact.ai.

It uses AI (specifically a 'Large Language Model' (LLM)) to summarize relevant text from reliable sources retrieved from the internet. Some things to consider when using it:

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