Srikant86/MVTamperBenchEnd
MVTamperBench Dataset Overview MVTamperBenchEnd 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 four distinct tampering techniques: Masking: Overlays a black rectangle on a 1-second segment, simulating visual data loss. Repetition: Repeats a 1-second segment, introducing temporal redundancy. Rotation:… See the full description on the dataset page: https://huggingface.co/datasets/Srikant86/MVTamperBenchEnd.
| Author / organization | Srikant86 |
|---|---|
| License (as declared) | mit |
| Downloads | 182 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 27 December 2024 |
| Last updated at source | 3 January 2025 |
Popularity, last 3 days
182 now (+3 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Recent changes
- 16 Oct 2026 Description: MVTamperBench Dataset Overview MVTamperBenchEnd 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, systematical → MVTamperBench Dataset Overview MVTamperBenchEnd 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, systematical
Changes detected by Silytics between refreshes of the source.
More from Srikant86
- Srikant86/VLMEvalDataset
- Srikant86/MVTamperBenchDataset
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.