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: Repeats… See the full description on the dataset page:

Source: Hugging Face Hub (Srikant86/MVTamperBench). Metadata imported from the dataset’s Hub tags.