Datasets:
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---
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. |