metadata

license: mit
extra_gated_prompt: >-
  You agree to not use the dataset to conduct experiments that cause harm to
  human subjects. Please note that the data in this dataset may be subject to
  other agreements. Before using the data, be sure to read the relevant
  agreements carefully to ensure compliant use. Video copyrights belong to the
  original video creators or platforms and are for academic research use only.
task_categories:
  - visual-question-answering
extra_gated_fields:
  Name: text
  Company/Organization: text
  Country: text
  E-Mail: text
modalities:
  - Video
  - Text
configs:
  - config_name: action_sequence
    data_files: json/action_sequence.json
  - config_name: moving_count
    data_files: json/moving_count.json
  - config_name: action_prediction
    data_files: json/action_prediction.json
  - config_name: episodic_reasoning
    data_files: json/episodic_reasoning.json
  - config_name: action_antonym
    data_files: json/action_antonym.json
  - config_name: action_count
    data_files: json/action_count.json
  - config_name: scene_transition
    data_files: json/scene_transition.json
  - config_name: object_shuffle
    data_files: json/object_shuffle.json
  - config_name: object_existence
    data_files: json/object_existence.json
  - config_name: unexpected_action
    data_files: json/unexpected_action.json
  - config_name: moving_direction
    data_files: json/moving_direction.json
  - config_name: state_change
    data_files: json/state_change.json
  - config_name: object_interaction
    data_files: json/object_interaction.json
  - config_name: character_order
    data_files: json/character_order.json
  - config_name: action_localization
    data_files: json/action_localization.json
  - config_name: counterfactual_inference
    data_files: json/counterfactual_inference.json
  - config_name: fine_grained_action
    data_files: json/fine_grained_action.json
  - config_name: moving_attribute
    data_files: json/moving_attribute.json
  - config_name: egocentric_navigation
    data_files: json/egocentric_navigation.json
language:
  - en
size_categories:
  - 1K<n<10K

Proposed MVTamperBench, a novel benchmark that systematically evaluates the adversarial robustness of VLMs against video specific tampering techniques, with a focus on temporal reasoning and multimodal coherence.

Dataset Description

MVTamperBench applies five distinct tampering techniques to the original MVBench videos: Dropping, Masking, Substitution, Repetition, and Rotation. Each tampering effect introduces unique adversarial challenges to test VLM robustness under various conditions

Tampering Techniques

Dropping: Removes a 1-second segment, creating temporal discontinuity.
Masking: Overlays a black rectangle on a 1-second segment, simulating visual data loss.
Rotation: Rotates a 1-second segment by 180 degrees, introducing spatial distortion.
Substitution: Replaces a 1-second segment with a random clip from another video, disrupting the temporal and contextual flow.
Repetition: Repeats a 1-second segment, introducing temporal redundancy.