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:

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