arXiv Computer Vision
Where the Real Clips Came From: Corpus Identity in AI-Generated Video Detection
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Jul 21
Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream
Detectors for AI-generated video are evaluated offline. A clip is decoded to pixels and scored once, increasingly by a large vision-language model.
arXiv Computer Vision
Sep 10
MotionBlind: Probing the Illusion of Motion Understanding in Video-LLMs
arXiv:2609.09528v1 Announce Type: new Abstract: Video large language models (Video-LLMs) are increasingly used as the perceptual front end of world models, a role that assumes they can read motion: h...
arXiv Computation and Language
Sep 4
Select, Compress, Reinvest: A Controlled Study of Visual-Token Allocation in Long-Video MLLMs
The study investigates how long‑video language models decide which frames to keep, compress, and reuse, testing each decision in isolation across six selection rules, three benchmarks, and two answering models. It finds that selecting frames based on queries yields the biggest performance boost, that halving spatial resolution costs little, and that reallocating saved tokens to more compressed frames can further improve accuracy. The work also highlights the importance of a unified evaluation harness to avoid misleading comparisons.
arXiv AI
Jul 7
SPLIT: Training-Free AI-Generated and Partially Edited Video Detection via Spatial Patch-Level Incoherence and Temporal Roughness
arXiv:2607. 02886v1 Announce Type: cross Abstract: Deploying AI-generated video detectors in real-world services demands an ultra-low false positive rate (FPR) on real videos to avoid falsely rejecting authentic content, a regime where standard metrics such as AUROC fail to reflect actual operating behavior.
arXiv Computer Vision
Sep 1
PERSIST: Persistent-State Discrimination for Shot Boundary Detection
PERSIST redefines shot boundary detection as a task of semantic discrimination, requiring a persistent update of a video’s latent temporal state rather than a transient visual change. It employs a FiLM‑conditioned sinusoidal representation network and a structured discriminator that fuses local change, transient impulse, and return‑to‑trend cues into a single interpretable per‑frame signal. The method achieves comparable recall to leading detectors while significantly reducing false positives from flash, text overlay, and archival artifacts, and it is trained solely on real transitions from ClipShots.
arXiv AI
Jul 16
Continuously Evolving Deepfake Detection: An Architecture and Public-Benchmark Evaluation of a Dynamic Detection System
arXiv:2607. 13234v1 Announce Type: cross Abstract: Deepfake detectors that achieve near-perfect scores on academic benchmarks collapse on real-world content: recent in-the-wild evaluations report AUC drops of 45-50% for state-of-the-art open-source models.