FakeI2V-Bench：评估图像级深度伪造检测器在深度伪造视频检测中的适用性
FakeI2V-Bench: Benchmarking the Applicability of Image-level Deepfake Detectors for Deepfake Video Detection
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中文总结 AI 辅助
本研究构建FakeI2V-Bench基准，评估图像级深度伪造检测器在视频检测中的性能，提出IV-Bridge框架聚合帧级预测，使多数图像级检测器性能超越现有视频级方法，为相关研究提供基准与新方向。
中文摘要 AI 辅助
近期视频生成模型的显著进展大幅加剧了深度伪造威胁，但当前的深度伪造视频检测基准仍不完善，尤其是图像级检测器在视频领域的有效性尚未得到系统评估。为填补这一空白，我们提出FakeI2V-Bench，这是一个用于评估挑战性场景下最先进视频级深度伪造检测器的基准，尤其侧重系统评估图像级深度伪造检测器在视频领域的性能。FakeI2V-Bench包含97548个视频，涵盖最新强大生成模型生成的内容及更广泛的类别。我们利用该数据集对8个视频级检测器和12个代表性图像级检测器进行系统评估，实验结果显示，性能最优的图像级检测器AUC达80.16%，略优于最强的视频级检测器（AUC为79.99%）。除基准测试外，我们还提出IV-Bridge，这是一个提升图像级深度伪造检测器在视频中适用性的通用框架，它采用带有统计特征的随机森林模型聚合帧级预测，使11个图像级检测器的性能超越最先进的视频级方法，其最优变体AUC达93.80%。总体而言，FakeI2V-Bench为深度伪造视频检测建立了严谨基准，并为将图像级检测器扩展至视频领域提供了新途径，为未来研究提供了新见解和方向。代码和数据可在指定URL获取。
英文摘要
Recent advances in video generation models have significantly intensified the deepfake threat, yet the current deepfake video detection benchmarks remain underdeveloped. In particular, the effectiveness of image-level detectors in the video domain has not been systematically assessed. To fill this gap, we present FakeI2V-Bench, a benchmark for evaluating state-of-the-art video-level deepfake detectors in challenging scenarios, with a particular focus on systematically assessing the performance of image-level deepfake detectors in the video domain. FakeI2V-Bench comprises 97,548 videos, containing content generated by the latest powerful generation models and covering a broader range of categories. Using this dataset, we conduct a systematic evaluation of eight video-level detectors and twelve representative image-level detectors. Experimental results show that the best-performing image-level detector achieves an 80.16% AUC, slightly outperforming the strongest video-level detector (i.e., 79.99% AUC). Going beyond benchmarking, we present IV-Bridge, a general framework that enhances the applicability of image-level deepfake detectors to videos. IV-Bridge employs a random forest model with statistical features to aggregate frame-level predictions, allowing eleven image-level detectors to surpass state-of-the-art video-level approaches, with the best-performing variant achieving a 93.80% AUC. Overall, FakeI2V-Bench establishes a rigorous benchmark for deepfake video detection and introduces a novel pathway for extending image-level detectors to the video domain, offering new insights and directions for future research. Code and data are available at https://github.com/CryptoAILab/FakeI2V-Bench.
发表机构
- School of Cyber Science and Technology, Shandong University（山东大学网络空间安全学院）
- School of Cryptologic Science and Engineering, Shandong University（山东大学密码技术与工程学院）
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