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: Repeats… See the full description on the dataset page: https://huggingface.co/datasets/Srikant86/MVTamperBench.
