Deepfake tweets automatic detection

• URL: http://arxiv.org/abs/2406.16489v1
• Date: Mon, 24 Jun 2024 09:55:31 GMT
• Title: Deepfake tweets automatic detection
• Authors: Adam Frej, Adrian Kaminski, Piotr Marciniak, Szymon Szmajdzinski, Soveatin Kuntur, Anna Wroblewska,
• Abstract summary: This study uses advanced natural language processing (NLP) techniques to distinguish between genuine and AI-generated texts. By developing reliable methods for detecting AI-generated misinformation, this work contributes to a more trustworthy online information environment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study addresses the critical challenge of detecting DeepFake tweets by leveraging advanced natural language processing (NLP) techniques to distinguish between genuine and AI-generated texts. Given the increasing prevalence of misinformation, our research utilizes the TweepFake dataset to train and evaluate various machine learning models. The objective is to identify effective strategies for recognizing DeepFake content, thereby enhancing the integrity of digital communications. By developing reliable methods for detecting AI-generated misinformation, this work contributes to a more trustworthy online information environment.

Related papers

• Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights [49.81915942821647]
  Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. This paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity.
  arXiv  Detail & Related papers  (2024-11-12T09:02:11Z)
• Evolving from Single-modal to Multi-modal Facial Deepfake Detection: A Survey [40.11614155244292]
  As AI-generated media become more realistic, the risk of misuse to spread misinformation and commit identity fraud increases. This work traces the evolution from traditional single-modality methods to sophisticated multi-modal approaches that handle audio-visual and text-visual scenarios. To our knowledge, this is the first survey of its kind.
  arXiv  Detail & Related papers  (2024-06-11T05:48:04Z)
• Who Writes the Review, Human or AI? [0.36498648388765503]
  This study proposes a methodology to accurately distinguish AI-generated and human-written book reviews. Our approach utilizes transfer learning, enabling the model to identify generated text across different topics. The experimental results demonstrate that it is feasible to detect the original source of text, achieving an accuracy rate of 96.86%.
  arXiv  Detail & Related papers  (2024-05-30T17:38:44Z)
• Deepfake Detection and the Impact of Limited Computing Capabilities [0.0]
  This work aims to address the detection of deepfakes across various existing datasets in a scenario with limited computing resources. The goal is to analyze the applicability of different deep learning techniques under these restrictions and explore possible approaches to enhance their efficiency.
  arXiv  Detail & Related papers  (2024-02-08T11:04:34Z)
• CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition [53.860796916196634]
  We propose a Deep Information Decomposition (DID) framework to enhance the performance of Cross-dataset Deepfake Detection (CrossDF) Unlike most existing deepfake detection methods, our framework prioritizes high-level semantic features over specific visual artifacts. It adaptively decomposes facial features into deepfake-related and irrelevant information, only using the intrinsic deepfake-related information for real/fake discrimination.
  arXiv  Detail & Related papers  (2023-09-30T12:30:25Z)
• Leveraging Deep Learning Approaches for Deepfake Detection: A Review [0.0]
  Deepfakes are fabricated media generated by AI that are difficult to set apart from the real media. This paper aims to explore different methodologies with an intention to achieve a cost-effective model.
  arXiv  Detail & Related papers  (2023-04-04T16:04:42Z)
• Can AI-Generated Text be Reliably Detected? [50.95804851595018]
  Large Language Models (LLMs) perform impressively well in various applications. The potential for misuse of these models in activities such as plagiarism, generating fake news, and spamming has raised concern about their responsible use. We stress-test the robustness of these AI text detectors in the presence of an attacker.
  arXiv  Detail & Related papers  (2023-03-17T17:53:19Z)
• Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else? [93.91375268580806]
  Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications. By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
  arXiv  Detail & Related papers  (2021-11-09T13:30:34Z)
• Machine Learning Explanations to Prevent Overtrust in Fake News Detection [64.46876057393703]
  This research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining.
  arXiv  Detail & Related papers  (2020-07-24T05:42:29Z)
• Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data [64.65952078807086]
  Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs) Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. We seek a proactive and sustainable solution on deepfake detection by introducing artificial fingerprints into the models.
  arXiv  Detail & Related papers  (2020-07-16T16:49:55Z)

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.