# VICTOR: Dataset Copyright Auditing in Video Recognition Systems Source: [VICTOR: Dataset Copyright Auditing in Video Recognition Systems]() Collection index: [llms.txt]() Data updated: 2025-12-18T05:00:04.345Z Authors: Quan Yuan, Zhikun Zhang, Linkang Du, Min Chen, Mingyang Sun, Yunjun Gao, Shibo He, Jiming Chen Published: 2025-12-16T14:26:01.000Z ## Abstract Video recognition systems are increasingly being deployed in daily life, such as content recommendation and security monitoring. To enhance video recognition development, many institutions have released high-quality public datasets with open-source licenses for training advanced models. At the same time, these datasets are also susceptible to misuse and infringement. Dataset copyright auditing is an effective solution to identify such unauthorized use. However, existing dataset copyright solutions primarily focus on the image domain; the complex nature of video data leaves dataset copyright auditing in the video domain unexplored. Specifically, video data introduces an additional temporal dimension, which poses significant challenges to the effectiveness and stealthiness of existing methods. In this paper, we propose VICTOR, the first dataset copyright auditing approach for video recognition systems. We develop a general and stealthy sample modification strategy that enhances the output discrepancy of the target model. By modifying only a small proportion of samples (e.g., 1%), VICTOR amplifies the impact of published modified samples on the prediction behavior of the target models. Then, the difference in the model's behavior for published modified and unpublished original samples can serve as a key basis for dataset auditing. Extensive experiments on multiple models and datasets highlight the superiority of VICTOR. Finally, we show that VICTOR is robust in the presence of several perturbation mechanisms to the training videos or the target models. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Dataset Auditing on HMDB-51](): Dataset Auditing; HMDB-51; Delta-acc: -0.02. - [Dataset Auditing on UCF-101](): Dataset Auditing; UCF-101; Delta-acc: -0.011. - [Dataset Auditing on SS v2](): Dataset Auditing; SS v2; True Positive Rate (TPR): 100. ## Original sources and implementation [Original paper]() [PDF]()