Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2603.26154 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915894791241728 |
|---|---|
| author | Li, Xiaofeng Sheng, Leyi Sun, Zhen Zhang, Zongmin Wei, Jiaheng He, Xinlei |
| author_facet | Li, Xiaofeng Sheng, Leyi Sun, Zhen Zhang, Zongmin Wei, Jiaheng He, Xinlei |
| contents | With the rapid advancement of image-to-video (I2V) generation models, their potential for misuse in creating malicious content has become a significant concern. For instance, a single image can be exploited to generate a fake video, which can be used to attract attention and gain benefits. This phenomenon is referred to as an I2V generation misuse. Existing image protection methods suffer from the absence of a unified benchmark, leading to an incomplete evaluation framework. Furthermore, these methods have not been systematically assessed in I2V generation scenarios and against preprocessing attacks, which complicates the evaluation of their effectiveness in real-world deployment scenarios.To address this challenge, we propose IP-Bench (Image Protection Bench), the first systematic benchmark designed to evaluate protection methods in I2V generation scenarios. This benchmark examines 6 representative protection methods and 5 state-of-the-art I2V models. Furthermore, our work systematically evaluates protection methods' robustness with two robustness attack strategies under practical scenarios and analyzes their cross-model & cross-modality transferability. Overall, IP-Bench establishes a systematic, reproducible, and extensible evaluation framework for image protection methods in I2V generation scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26154 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IP-Bench: Benchmark for Image Protection Methods in Image-to-Video Generation Scenarios Li, Xiaofeng Sheng, Leyi Sun, Zhen Zhang, Zongmin Wei, Jiaheng He, Xinlei Computer Vision and Pattern Recognition With the rapid advancement of image-to-video (I2V) generation models, their potential for misuse in creating malicious content has become a significant concern. For instance, a single image can be exploited to generate a fake video, which can be used to attract attention and gain benefits. This phenomenon is referred to as an I2V generation misuse. Existing image protection methods suffer from the absence of a unified benchmark, leading to an incomplete evaluation framework. Furthermore, these methods have not been systematically assessed in I2V generation scenarios and against preprocessing attacks, which complicates the evaluation of their effectiveness in real-world deployment scenarios.To address this challenge, we propose IP-Bench (Image Protection Bench), the first systematic benchmark designed to evaluate protection methods in I2V generation scenarios. This benchmark examines 6 representative protection methods and 5 state-of-the-art I2V models. Furthermore, our work systematically evaluates protection methods' robustness with two robustness attack strategies under practical scenarios and analyzes their cross-model & cross-modality transferability. Overall, IP-Bench establishes a systematic, reproducible, and extensible evaluation framework for image protection methods in I2V generation scenarios. |
| title | IP-Bench: Benchmark for Image Protection Methods in Image-to-Video Generation Scenarios |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.26154 |