The article reports on a survey of 21 experts—including AI researchers, policymakers, and disinformation specialists—about the risks posed by Generative Artificial Intelligence (GenAI) to information integrity. It finds that while deepfake videos are immediately shocking, large‑scale text generation threatens to create systemic epistemic fragmentation and synthetic consensus, especially in politics. Experts express doubt about current detection tools and instead advocate for provenance standards and regulatory frameworks, emphasizing the need for reproducible research methods and standardized benchmarks to effectively counter synthetic media.
arXiv:2609.25017v1 Announce Type: new Abstract: Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains...
arXiv:2606. 00621v1 Announce Type: cross Abstract: Generative artificial intelligence has fundamentally changed how content is now produced.
arXiv:2501. 14728v2 Announce Type: replace-cross Abstract: While generative artificial intelligence (GenAI) models have achieved significant success, their misuse for generating deceptive content raises growing concerns about online information security.
arXiv:2606. 03348v1 Announce Type: cross Abstract: Recent generative models can now produce visual artifacts with realistic embedded text and layouts, creating a new misinformation threat: synthetic credibility.
The paper "Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems" updates the 2024 survey on generative AI and misinformation, shifting from a literature review to practical countermeasures. It documents changes in the threat landscape, noting that Large Language Models and multimodal systems have improved AI-generated content, thereby worsening information quality. The authors introduce JudgeGPT, a platform for assessing human perception of AI-generated news, and RogueGPT, a controlled stimulus generation engine, forming an experimental pipeline to study detection and mitigation strategies, including LLM-based detection and inoculation approaches.
The paper introduces Provenance Density, an interface that visualizes the density of verified claims within a text to counter the Fluency Trap—where users mistake fluent AI-generated hallucinations for truth. In a study with 81 participants, the interface significantly improved users’ ability to distinguish true from fabricated content, while no signal led to no discernment. A technical audit of 200 samples revealed that retrieval density alone is insufficient, and that the Consistency Veto provides most of the discriminative power for dynamic queries.
arXiv:2606. 28510v1 Announce Type: cross Abstract: Across social and online platforms, people are increasingly exposed to AI-generated images.
arXiv:2607. 22745v1 Announce Type: cross Abstract: Rapid advances in image generation are eroding the evidentiary value of visual content in settings where authenticity can affect public safety and personal reputation.
arXiv:2606. 07613v1 Announce Type: cross Abstract: Visual evidence has long been treated as a reliable form of legal proof, but advances in artificial intelligence (AI) are undermining that assumption.
arXiv:2606. 28331v1 Announce Type: cross Abstract: The widespread deployment of generative artificial intelligence (AI) models has raised serious concerns about the proliferation of AI-generated content.
arXiv:2608. 10773v1 Announce Type: cross Abstract: The increasing use of Generative Artificial Intelligence (GenAI) in journalism raises concerns about possible detrimental effects both on journalism and its democratic function.
The increasing use of Generative Artificial Intelligence (GenAI) in journalism raises concerns about possible detrimental effects both on journalism and its democratic function. We explore these risks through a case study of GenAI in Norwegian Newsrooms during the 2025 parliamentary election campaign.