Revealing Artifacts via Noise Amplification: A Novel Perspective for AI-Generated Video Detection

A new detection method called Noise Amplification can distinguish AI-generated videos from real footage by magnifying subtle artifacts invisible to the human eye, according to a paper submitted to arXiv on 15 Jun 2026 [1]. The approach outperforms existing techniques on two benchmarks, including a newly introduced challenging test set [2]. The paper, titled "Revealing Artifacts via Noise Amplification: A Novel Perspective for AI-Generated Video Detection," addresses a gap in current research, which has largely focused on videos created by generative adversarial networks rather than newer text-to-video models [1][2]. The authors note that while state-of-the-art text-to-video models produce realistic visual content, they fail to replicate the fine details and the way those details change over time that are present in authentic footage [2]. The Noise Amplification method operates by extracting noise signals from bit-planes — a representation that captures the subtle details or noise within images and video frames [2]. These signals are then amplified through a three-stage process: pixel-level intensity enhancement, region-level spatial amplification, and frame-level temporal aggregation, before being fed into a classifier [1][2]. To rigorously test the method, the researchers used GenVidBench, a large-scale dataset, and created a new benchmark called HardGVD, designed to evaluate detection in more challenging scenarios [1][2]. The paper states that the Noise Amplification approach "significantly outperforms state-of-the-art methods" on both benchmarks [2]. The research was posted on arXiv, an open-access repository for electronic preprints that, as of late 2024, receives about 24,000 submissions per month and hosts over two million articles [7]. The paper's abstract page features integrations from arXivLabs, a framework launched by arXiv to allow community collaborators to develop and share experimental tools directly on the site [5][6]. These tools, which appear as tabs on the article record page, include services such as the Bibliographic Explorer for navigating citation trees and the CORE Recommender for finding related open-access papers [5][6]. arXiv has stated that third-party collaborators in the Labs program only have access to minimal and anonymized user data, and any other use is prohibited without written consent [5].