# ATSS: Detecting AI-Generated Videos via Anomalous Temporal Self-Similarity

arXiv: https://arxiv.org/abs/2604.04029 · HTML: https://mlsanity.com/paper/2604.04029/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 71 references that are in the map.
- First posted on arXiv 5 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [ModelScope Text-to-Video Technical Report](https://mlsanity.com/paper/2308.06571/) (2023)
- [VideoCrafter1: Open Diffusion Models for High-Quality Video Generation](https://mlsanity.com/paper/2310.19512/) (2023)
- [VideoPhy: Evaluating Physical Commonsense for Video Generation](https://mlsanity.com/paper/2406.03520/) (2024)
- [DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark](https://mlsanity.com/paper/2405.19707/) (2024)
- [Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks](https://mlsanity.com/paper/2306.04362/) (2023)
- [DreaMoving: A Human Video Generation Framework based on Diffusion Models](https://mlsanity.com/paper/2312.05107/) (2023)
- [Distinguish Any Fake Videos: Unleashing the Power of Large-scale Data and Motion Features](https://mlsanity.com/paper/2405.15343/) (2024)
- [Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos](https://mlsanity.com/paper/2406.09601/) (2024)
- [Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes](https://mlsanity.com/paper/2409.10889/) (2024)

## Built on by (most cited first, 1 of 1)

- [Auditing Generalization in AI-Generated Video Detection: A Six-Control Protocol and the VidAudit Toolkit](https://mlsanity.com/paper/2606.31004/) (2026)

## Abstract (arXiv)

AI-generated videos (AIGVs) have achieved unprecedented photorealism, posing severe threats to digital forensics. Existing AIGV detectors focus mainly on localized artifacts or short-term temporal inconsistencies, thus often fail to capture the underlying generative logic governing global temporal evolution, limiting AIGV detection performance. In this paper, we identify a distinctive fingerprint in AIGVs, termed anomalous temporal self-similarity (ATSS). Unlike real videos that exhibit stochastic natural dynamics, AIGVs follow deterministic anchor-driven trajectories (e.g., text or image prompts), inducing unnaturally repetitive correlations across visual and semantic domains. To exploit this, we propose the ATSS method, a multimodal detection framework that exploits this insight via a triple-similarity representation and a cross-attentive fusion mechanism. Specifically, ATSS reconstructs semantic trajectories by leveraging frame-wise descriptions to construct visual, textual, and cross-modal similarity matrices, which jointly quantify the inherent temporal anomalies. These matrices are encoded by dedicated Transformer encoders and integrated via a bidirectional cross-attentive fusion module to effectively model intra- and inter-modal dynamics. Extensive experiments on four large-scale benchmarks, including GenVideo, EvalCrafter, VideoPhy, and VidProM, demonstrate that ATSS significantly outperforms state-of-the-art methods in terms of AP, AUC, and ACC metrics, exhibiting superior generalization across diverse video generation models. Code and models of ATSS will be released at https://github.com/hwang-cs-ime/ATSS.