[2511.12834] SAGA: Source Attribution of Generative AI Videos

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Computer Science > Computer Vision and Pattern Recognition

arXiv:2511.12834 (cs)

[Submitted on 16 Nov 2025 (v1), last revised 2 Apr 2026 (this version, v2)]

Title:SAGA: Source Attribution of Generative AI Videos

Authors:Rohit Kundu, Vishal Mohanty, Hao Xiong, Shan Jia, Athula Balachandran, Amit K. Roy-Chowdhury<br>View a PDF of the paper titled SAGA: Source Attribution of Generative AI Videos, by Rohit Kundu and 5 other authors

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Abstract:The proliferation of generative AI has led to hyper-realistic synthetic videos, escalating misuse risks and outstripping binary real/fake detectors. We introduce SAGA (Source Attribution of Generative AI videos), the first comprehensive framework to address the urgent need for AI-generated video source attribution at a large scale. Unlike traditional detection, SAGA identifies the specific generative model used. It uniquely provides multi-granular attribution across five levels: authenticity, generation task (e.g., T2V/I2V), model version, development team, and the precise generator, offering far richer forensic insights. Our novel video transformer architecture, leveraging features from a robust vision foundation model, effectively captures spatio-temporal artifacts. Critically, we introduce a data-efficient pretrain-and-attribute strategy, enabling SAGA to achieve state-of-the-art attribution using only 0.5\% of source-labeled data per class, matching fully supervised performance. Furthermore, we propose Temporal Attention Signatures (T-Sigs), a novel interpretability method that visualizes learned temporal differences, offering the first explanation for why different video generators are distinguishable. Extensive experiments on public datasets, including cross-domain scenarios, demonstrate that SAGA sets a new benchmark for synthetic video provenance, providing crucial, interpretable insights for forensic and regulatory applications.

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2511.12834 [cs.CV]

(or<br>arXiv:2511.12834v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2511.12834

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arXiv-issued DOI via DataCite

Submission history<br>From: Rohit Kundu [view email]<br>[v1]<br>Sun, 16 Nov 2025 23:39:54 UTC (11,337 KB)

[v2]<br>Thu, 2 Apr 2026 18:07:08 UTC (11,325 KB)

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