arXiv:2608. 06732v1 Announce Type: new Abstract: Recent text-to-video (T2V) generation models enable fake news videos to be synthesized from scratch, shifting the threat beyond cheap fakes assembled from existing footage.
arXiv:2606. 00101v1 Announce Type: cross Abstract: With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security.
arXiv:2512.20257v2 Announce Type: replace Abstract: With the rise of easily accessible generative tools for creating and manipulating multimedia content, the threat of realistic synthetic alterations...
arXiv:2608. 06865v1 Announce Type: cross Abstract: The malicious use of generative artificial intelligence to create highly realistic deepfake videos raises serious ethical concerns and poses substantial challenges to AI safety.
arXiv:2502.14994v2 Announce Type: replace Abstract: The rapid advancement of AI-generated video poses challenges to digital authenticity and security. Current detection methods, often trained on spec...
arXiv:2609.36850v1 Announce Type: new Abstract: Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipul...
arXiv:2606. 16742v1 Announce Type: cross Abstract: With the rapid advancement of video generation models, distinguishing between AI-generated and authentic videos has emerged as a challenging endeavor.
arXiv:2603. 23916v3 Announce Type: replace-cross Abstract: Multimodal deception detection aims to identify deceptive behavior by analyzing audiovisual cues for forensics and security.
OmniVBench introduces a comprehensive benchmark and a large-scale dataset for omni reference-to-video (R2V) generation, addressing gaps in existing evaluations that focus only on limited reference types and holistic consistency. The benchmark expands evaluation across 7 task families and 18 fine-grained tasks, covering content, motion, style, structure, narrative, and multi-reference settings, and employs a factor‑grounded evaluation with 12,172 checklist items to assess preservation, disentanglement, and routing of reference factors. The accompanying Omni‑R2V Dataset provides 340K training samples derived from professional video footage, along with task‑specific pipelines for scalable data construction, enabling broader research and revealing performance gaps in current R2V models.
arXiv:2601. 14954v3 Announce Type: replace Abstract: Social media increasingly disseminates information through mixed image text posts, but rumors often exploit subtle inconsistencies and forged content, making detection based solely on post content difficult.
arXiv:2606. 07651v1 Announce Type: new Abstract: Traditional fake news detection methods are falling behind as multimodal misinformation grows more advanced, seamlessly blending deceptive text, manipulated visuals, and factually incorrect claims.
Video-HolmesV2 is a new benchmark that tests multimodal large language models on their ability to reason with spatio‑temporal audio‑visual evidence in long videos. It requires models to justify answers with precise evidence, uses a multi‑model cross‑verification pipeline and a spatio‑temporal evidence‑aware metric, and introduces an audio‑text guided token compression framework to reduce long‑context noise. In evaluations, even strong proprietary models score below 60% while the proposed approach outperforms comparable open‑source omni‑models.
Large vision-language models (LVLMs) have recently shown immense potential in automated content moderation, sparking growing interest in developing harmful-video benchmarks. However, we identify two primary limitations in existing works: 1) The multi-layered characteristics of harmful videos are overlooked.