(cache)Detecting AI-Generated Video via Frame Consistency | IEEE Conference Publication | IEEE Xplore

Detecting AI-Generated Video via Frame Consistency


Abstract:

The increasing realism of AI-generated videos has raised potential security concerns, making it difficult for humans to distinguish them from the naked eye. Despite these...Show More

Abstract:

The increasing realism of AI-generated videos has raised potential security concerns, making it difficult for humans to distinguish them from the naked eye. Despite these concerns, limited research has been dedicated to detecting such videos effectively. To this end, we propose an open-source AI-generated video detection dataset. Our dataset spans diverse objects, scenes, behaviors, and actions by organizing input prompts into independent dimensions. It also includes various generation models with different generative models, featuring popular commercial models such as OpenAI’s Sora, Google’s Veo, and Kwai’s Kling. Furthermore, we propose a simple yet effective Detection model based on Concistency of Frame (DeCoF), which learns robust temporal artifacts across different generation methods. Extensive experiments demonstrate the generality and efficacy of the proposed DeCoF in detecting AI-generated videos, including those from nowadays’ mainstream commercial generators.
Date of Conference: 30 June 2025 - 04 July 2025
Date Added to IEEE Xplore: 30 October 2025
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ISSN Information:

Conference Location: Nantes, France

I. Introduction

Existing generative models such as Diffusion models [1]–[3] have excelled in generating high-quality visual contents, driving advancements in video generation [4], [5] that produce highly realistic videos capable of deceiving the human eye. Unfortunately, these advancements have raised significant security concerns, such as privacy violations and the erosion of trust on social media [6], [7], underscoring the urgent need for effective and reliable detection of AI-generated videos.

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References

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