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D3: Training-Free AI-Generated Video Detection Using Second-Order Features

Created by
  • Haebom

Author

Chende Zheng, Ruiqi Suo, Chenhao Lin, Zhengyu Zhao, Le Yang, Shuai Liu, Minghui Yang, Cong Wang, Chao Shen

Outline

This paper presents a novel detection technique that overcomes the limitations of existing detection methodologies to address the growing challenge of AI-generated videos. We establish a theoretical framework based on second-order dynamics analysis under Newtonian mechanics and extend the second-order central difference feature for temporal artifact detection. This approach reveals fundamental differences in the distribution of second-order features between real and AI-generated videos, and we propose a new detection method, Detection by Difference of Differences (D3), which requires no training. We validate the superiority of D3 on four open-source datasets (Gen-Video, VideoPhy, EvalCrafter, and VidProM), demonstrating a 10.39% improvement in average precision over the best-performing existing method. Furthermore, we experimentally demonstrate its computational efficiency and robustness.

Takeaways, Limitations

Takeaways:
We present a new theoretical foundation for AI-generated video detection by leveraging second-order dynamic analysis based on Newtonian mechanics.
Proposal of D3, an efficient detection method that requires no training, and verification of its excellent performance.
Verifying D3's robustness and generalization performance across various datasets.
Providing a detection method with high computational efficiency and strong robustness.
Limitations:
Further research is needed to determine whether the method presented in this paper guarantees the same performance for all types of AI-generated videos.
There is a need to review the adaptability of AI-generated video technology, which will become more sophisticated and diverse in the future.
The possibility of bias towards certain types of temporal artifacts.
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