Enterprise AI Analysis
GenVidBench: A Challenging Benchmark for Detecting AI-Generated Video
Explore how GenVidBench addresses the critical need for robust AI-generated video detection, providing a challenging dataset for developing next-generation models to combat misinformation and enhance digital trust.
Executive Impact & Key Findings
The GenVidBench dataset pushes the boundaries of AI-generated video detection, revealing critical insights for enterprise-grade solutions.
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Total Videos for Detection
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State-of-the-Art Generators Covered
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Top Accuracy (MVIT V2, Cross-Gen)
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SlowFast Acc. on GenVidBench (Challenging)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
GenVidBench is introduced as a challenging AI-generated video detection dataset designed to overcome limitations of existing benchmarks. It comprises 143,131 videos, including real content from Vript and HD-VG-130M, and AI-generated videos from 8 state-of-the-art models like Mora, MuseV, SVD, and Pika. A key design principle is the cross-source and cross-generator setting, where training and test sets are derived from different generation sources and generators, preventing detectors from overfitting to specific video attributes and ensuring robust generalization.
Furthermore, the dataset provides rich semantic content labels—categorized by objects, actions, and locations. This multi-dimensional classification allows researchers to develop scenario-specific detectors and analyze how different content types influence detection difficulty, fostering more nuanced model development.
Initial evaluations with state-of-the-art video classification models like VideoSwin, UniFormerV2, and MVIT V2 on GenVidBench reveal significant challenges. The highest accuracy achieved in the cross-source and cross-generator task is 79.90% by MVIT V2, indicating substantial room for improvement in AI-generated video detection. Notably, real videos are generally easier to distinguish, but AI-generated videos from SVD and those depicting 'Plants' are particularly difficult for current models.
The study also highlights how generation sources (text vs. image prompts) influence video attributes and detection difficulty. The explicit semantic tags allow for in-depth 'hard case analysis,' identifying categories like 'Plants' as particularly challenging. This detailed performance breakdown provides a crucial foundation for future research in developing more robust and generalizable AI-generated video detectors.
For enterprises, the proliferation of high-quality AI-generated videos poses significant risks, including misinformation spread, reputational damage, and cybersecurity threats. GenVidBench offers a vital tool for developing and evaluating AI-powered solutions to these challenges. By providing a diverse, challenging, and semantically rich dataset, it enables organizations to build robust detection systems capable of identifying sophisticated deepfake and AI-generated content in real-world, unpredictable scenarios.
The insights from GenVidBench can guide strategic investments in AI ethics, content moderation, and cybersecurity. Developing detectors that perform well on this benchmark signifies a model's ability to handle diverse generative models and content types, which is crucial for protecting brand integrity and ensuring trusted digital interactions across various enterprise applications.
143k+
Videos with Rich Semantic Labels
GenVidBench is the first 100k-level dataset that provides rich semantic content labels, categorized into objects, actions, and locations, ensuring diversity and aiding in the development of more generalized detection models.
GenVidBench Dataset Construction Flow
Real Video Sourcing (Vript, HD-VG-130M)
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Prompt/Image Extraction
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AI Video Generation (8 SOTA Models)
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Multi-Dimensional Semantic Tagging (Objects, Actions, Locations)
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Challenging Pair Formation (Same Source, Different Generators)
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GenVidBench Dataset Release
| Dataset | SlowFast Accuracy |
|---|---|
| DeepFakes [12] | 97.53% |
| Face2Face [34] | 94.93% |
| FaceSwap [19] | 95.01% |
| NeuralTextures [33] | 82.55% |
| GVF [25] | 60.95% |
| GenVidBench (Ours) | 41.66% |
Hard Case Analysis: Detecting AI-Generated Plants
The 'Plants' category emerged as the most difficult scenario for AI-generated video detection across various models. As highlighted in our analysis (Table 7), SVD-generated videos involving plants are most likely to be misclassified. Even top-performing models like TimeSformer (Table 8) achieved only 75.09% accuracy on the plant class, which is notably lower than their general performance. This suggests that AI models struggle with rendering intricate details, textures, and subtle movements of natural elements like plants, making them harder to distinguish from real videos. This finding points to a critical area for future research and generator improvement.
Calculate Your Potential AI Impact
Estimate the efficiency gains and cost savings by implementing advanced AI solutions derived from robust detection benchmarks.
Annual Cost Savings (Estimated)
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Hours Reclaimed Annually
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Your AI Implementation Roadmap
A structured approach to integrating cutting-edge AI detection capabilities into your enterprise operations.
Phase 1: Dataset Integration & Baseline Establishment
Integrate GenVidBench into existing AI detection pipelines. Establish baseline performance using current state-of-the-art models on the cross-source and cross-generator tasks. Analyze initial performance metrics to identify immediate areas for improvement.
Phase 2: Model Adaptation & Robustness Training
Develop or fine-tune detection models specifically to address the cross-source and cross-generator challenge. Focus on techniques that enhance generalization capabilities across diverse AI video generators and content attributes, leveraging the dataset's scale and diversity.
Phase 3: Semantic-Aware Detector Development
Utilize GenVidBench’s rich semantic labels (objects, actions, locations) to train scenario-specific detection models. Investigate how performance varies across different content categories and build specialized modules to improve accuracy in 'hard cases' like AI-generated plants.
Phase 4: Real-World Deployment & Monitoring
Deploy enhanced AI-generated video detectors in controlled real-world environments. Continuously monitor performance against newly emerging AI generation models and evolving deepfake techniques. Gather feedback to refine and retrain models.
Phase 5: Ethical AI & Policy Integration
Collaborate with ethics and policy experts to integrate robust AI-generated content detection into broader organizational guidelines. Ensure transparent reporting of AI-generated content and contribute to industry best practices for combating misinformation and protecting digital integrity.
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