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: Rotates a… See the full description on the dataset page: https://huggingface.co/datasets/Srikant86/MVTamperBenchEnd.