A Comprehensive Content Verification System for ensuring Digital Integrity in the Age of Deep Fakes
- URL: http://arxiv.org/abs/2411.19750v1
- Date: Fri, 29 Nov 2024 14:47:47 GMT
- Title: A Comprehensive Content Verification System for ensuring Digital Integrity in the Age of Deep Fakes
- Authors: RaviKanth Kaja,
- Abstract summary: This paper discusses a solution, a Content Verification System, designed to authenticate images and videos shared as posts or stories across the digital landscape.<n>Going beyond the limitations of blue ticks, this system empowers individuals and influencers to validate the authenticity of their digital footprint, safeguarding their reputation in an interconnected world.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In an era marked by the widespread sharing of digital content, the need for a robust content-integrity verification goes beyond the confines of individual social media platforms. While verified profiles (such as blue ticks on platforms like Instagram and X) have become synonymous with credibility, the content they share often traverses a complex network of interconnected platforms, by means of re-sharing, re-posting, etc., leaving a void in the authentication process of the content itself. With the advent of easily accessible AI tools (like DALL-E, Sora, and the tools that are explicitly built for generating deepfakes & face swaps), the risk of misinformation through social media platforms is growing exponentially. This paper discusses a solution, a Content Verification System, designed to authenticate images and videos shared as posts or stories across the digital landscape. Going beyond the limitations of blue ticks, this system empowers individuals and influencers to validate the authenticity of their digital footprint, safeguarding their reputation in an interconnected world.
Related papers
- Provenance Verification of AI-Generated Images via a Perceptual Hash Registry Anchored on Blockchain [0.0]
This paper proposes a blockchain-backed framework for verifying AI-generated images through a registry-based provenance mechanism.<n>The proposed system does not aim to universally detect all synthetic images, but instead focuses on verifying the provenance of AI-generated content that has been registered at creation time.
arXiv Detail & Related papers (2026-02-02T18:13:09Z) - User Negotiations of Authenticity, Ownership, and Governance on AI-Generated Video Platforms: Evidence from Sora [3.6795902817860693]
This study examines how users make sense of AI-generated videos on OpenAI's Sora.<n>We identify four dynamics that characterize how users negotiate authenticity, authorship, and platform governance.
arXiv Detail & Related papers (2025-12-05T08:23:27Z) - A New Incentive Model For Content Trust [0.0]
This paper outlines an incentive-driven and decentralized approach to verifying the veracity of digital content at scale.<n>We believe that it could be possible to foster a self-propelling paradigm shift to combat misinformation through a community-based governance model.
arXiv Detail & Related papers (2025-07-14T06:41:55Z) - Community Moderation and the New Epistemology of Fact Checking on Social Media [124.26693978503339]
Social media platforms have traditionally relied on independent fact-checking organizations to identify and flag misleading content.<n>X (formerly Twitter) and Meta have shifted towards community-driven content moderation by launching their own versions of crowd-sourced fact-checking.<n>We examine the current approaches to misinformation detection across major platforms, explore the emerging role of community-driven moderation, and critically evaluate both the promises and challenges of crowd-checking at scale.
arXiv Detail & Related papers (2025-05-26T14:50:18Z) - RealSeal: Revolutionizing Media Authentication with Real-Time Realism Scoring [0.27624021966289597]
Existing methods for watermarking synthetic data fall short, as they can be easily removed or altered.
Provenance techniques, which rely on metadata to verify content origin, fail to address the fundamental problem of staged or fake media.
This paper introduces a groundbreaking paradigm shift in media authentication by advocating for the watermarking of real content at its source.
arXiv Detail & Related papers (2024-11-26T18:48:23Z) - Fingerprinting and Tracing Shadows: The Development and Impact of Browser Fingerprinting on Digital Privacy [55.2480439325792]
Browser fingerprinting is a growing technique for identifying and tracking users online without traditional methods like cookies.
This paper gives an overview by examining the various fingerprinting techniques and analyzes the entropy and uniqueness of the collected data.
arXiv Detail & Related papers (2024-11-18T20:32:31Z) - Deepfake Media Forensics: State of the Art and Challenges Ahead [51.33414186878676]
AI-generated synthetic media, also called Deepfakes, have influenced so many domains, from entertainment to cybersecurity.
Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques.
This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.
arXiv Detail & Related papers (2024-08-01T08:57:47Z) - Bridging Social Media and Search Engines: Dredge Words and the Detection of Unreliable Domains [3.659498819753633]
We develop a website credibility classification and discovery system that integrates webgraph and social media contexts.
We introduce the concept of dredge words, terms or phrases for which unreliable domains rank highly on search engines.
We release a novel dataset of dredge words, highlighting their strong connections to both social media and online commerce platforms.
arXiv Detail & Related papers (2024-06-17T11:22:04Z) - Content Moderation on Social Media in the EU: Insights From the DSA
Transparency Database [0.0]
Digital Services Act (DSA) requires large social media platforms in the EU to provide clear and specific information whenever they restrict access to certain content.
Statements of Reasons (SoRs) are collected in the DSA Transparency Database to ensure transparency and scrutiny of content moderation decisions.
We empirically analyze 156 million SoRs within an observation period of two months to provide an early look at content moderation decisions of social media platforms in the EU.
arXiv Detail & Related papers (2023-12-07T16:56:19Z) - Countering Malicious Content Moderation Evasion in Online Social
Networks: Simulation and Detection of Word Camouflage [64.78260098263489]
Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems.
This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content.
arXiv Detail & Related papers (2022-12-27T16:08:49Z) - Fighting Malicious Media Data: A Survey on Tampering Detection and
Deepfake Detection [115.83992775004043]
Recent advances in deep learning, particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost.
This paper provides a comprehensive review of the current media tampering detection approaches, and discusses the challenges and trends in this field for future research.
arXiv Detail & Related papers (2022-12-12T02:54:08Z) - Detecting fake accounts through Generative Adversarial Network in online
social media [0.0]
This paper proposes a novel method using user similarity measures and the Generative Adversarial Network (GAN) algorithm to identify fake user accounts in the Twitter dataset.
Despite the problem's complexity, the method achieves an AUC rate of 80% in classifying and detecting fake accounts.
arXiv Detail & Related papers (2022-10-25T10:20:27Z) - The Impact of Disinformation on a Controversial Debate on Social Media [1.299941371793082]
We study how pervasive is the presence of disinformation in the Italian debate around immigration on Twitter.
By characterising the Twitter users with an textitUntrustworthiness score, we are able to see that such bad information consumption habits are not equally distributed across the users.
arXiv Detail & Related papers (2021-06-30T10:29:07Z) - Biometrics: Trust, but Verify [49.9641823975828]
Biometric recognition has exploded into a plethora of different applications around the globe.
There are a number of outstanding problems and concerns pertaining to the various sub-modules of biometric recognition systems.
arXiv Detail & Related papers (2021-05-14T03:07:25Z) - SoMin.ai: Personality-Driven Content Generation Platform [60.49416044866648]
We showcase the World's first personality-driven marketing content generation platform, called SoMin.ai.
The platform combines deep multi-view personality profiling framework and style generative adversarial networks.
It can be used for the enhancement of the social networking user experience as well as for content marketing routines.
arXiv Detail & Related papers (2020-11-30T08:33:39Z) - Quantifying the Vulnerabilities of the Online Public Square to Adversarial Manipulation Tactics [43.98568073610101]
We use a social media model to quantify the impacts of several adversarial manipulation tactics on the quality of content.
We find that the presence of influential accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation.
These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
arXiv Detail & Related papers (2019-07-13T21:12:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.