Srikant86/MVTamperBench
MVTamperBench Dataset Overview MVTamperBench is a robust benchmark designed to evaluate Vision-Language Models (VLMs) against adversarial video tampering effects. It leverages the diverse and well-structured MVBench dataset, systematically augmented with five distinct tampering techniques: Frame Dropping: Removes a 1-second segment, creating temporal discontinuity. Masking: Overlays a black rectangle on a 1-second segment, simulating visual data loss. Repetition:… See the full description on the dataset page: https://huggingface.co/datasets/Srikant86/MVTamperBench.
| Author / organization | Srikant86 |
|---|---|
| License (as declared) | mit |
| Downloads | 192 (as reported by Hugging Face) |
| Likes | 1 |
| Created | 9 November 2024 |
| Last updated at source | 29 January 2025 |
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Recent changes
- 16 Oct 2026 Description: MVTamperBench Dataset Overview MVTamperBench is a robust benchmark designed to evaluate Vision-Language Models (VLMs) against adversarial video tampering effects. It leverages the diverse and well-structured MVBench dataset, systematically → MVTamperBench Dataset Overview MVTamperBench is a robust benchmark designed to evaluate Vision-Language Models (VLMs) against adversarial video tampering effects. It leverages the diverse and well-structured MVBench dataset, systematically
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