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--- |
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license: mit |
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extra_gated_prompt: >- |
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You agree to not use the dataset to conduct experiments that cause harm to |
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human subjects. Please note that the data in this dataset may be subject to |
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other agreements. Before using the data, be sure to read the relevant |
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agreements carefully to ensure compliant use. Video copyrights belong to the |
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original video creators or platforms and are for academic research use only. |
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task_categories: |
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- visual-question-answering |
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extra_gated_fields: |
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Name: text |
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Company/Organization: text |
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Country: text |
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E-Mail: text |
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modalities: |
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- Video |
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- Text |
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configs: |
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- config_name: action_sequence |
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data_files: json/action_sequence.json |
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- config_name: moving_count |
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data_files: json/moving_count.json |
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- config_name: action_prediction |
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data_files: json/action_prediction.json |
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- config_name: episodic_reasoning |
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data_files: json/episodic_reasoning.json |
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- config_name: action_antonym |
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data_files: json/action_antonym.json |
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- config_name: action_count |
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data_files: json/action_count.json |
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- config_name: scene_transition |
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data_files: json/scene_transition.json |
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- config_name: object_shuffle |
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data_files: json/object_shuffle.json |
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- config_name: object_existence |
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data_files: json/object_existence.json |
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- config_name: unexpected_action |
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data_files: json/unexpected_action.json |
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- config_name: moving_direction |
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data_files: json/moving_direction.json |
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- config_name: state_change |
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data_files: json/state_change.json |
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- config_name: object_interaction |
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data_files: json/object_interaction.json |
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- config_name: character_order |
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data_files: json/character_order.json |
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- config_name: action_localization |
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data_files: json/action_localization.json |
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- config_name: counterfactual_inference |
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data_files: json/counterfactual_inference.json |
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- config_name: fine_grained_action |
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data_files: json/fine_grained_action.json |
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- config_name: moving_attribute |
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data_files: json/moving_attribute.json |
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- config_name: egocentric_navigation |
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data_files: json/egocentric_navigation.json |
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language: |
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- en |
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size_categories: |
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- 1K<n<10K |
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--- |
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# MVTamperBench Dataset |
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## Overview |
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**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: |
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1. **Masking**: Overlays a black rectangle on a 1-second segment, simulating visual data loss. |
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2. **Repetition**: Repeats a 1-second segment, introducing temporal redundancy. |
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3. **Rotation**: Rotates a 1-second segment by 180 degrees, introducing spatial distortion. |
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4. **Substitution**: Replaces a 1-second segment with a random clip from another video, disrupting the temporal and contextual flow. |
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The tampering effects are applied to the middle of each video to ensure consistent evaluation across models. |
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--- |
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## Dataset Details |
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The MVTamperBenchEnd dataset is built upon the **MVBench dataset**, a widely recognized collection used in video-language evaluation. It features a broad spectrum of content to ensure robust model evaluation, including: |
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- **Content Diversity**: Spanning a variety of objects, activities, and settings. |
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- **Temporal Dynamics**: Videos with temporal dependencies for coherence testing. |
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- **Benchmark Utility**: Recognized datasets enabling comparisons with prior work. |
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### Incorporated Datasets |
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The MVTamperBenchEnd dataset integrates videos from several sources, each contributing unique characteristics: |
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| Dataset Name | Primary Scene Type and Unique Characteristics | |
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|----------------------|-------------------------------------------------------------------------| |
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| STAR | Indoor actions and object interactions | |
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| PAXION | Real-world scenes with nuanced actions | |
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| Moments in Time (MiT) V1 | Indoor/outdoor scenes across varied contexts | |
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| FunQA | Humor-focused, creative, real-world events | |
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| CLEVRER | Simulated scenes for object movement and reasoning | |
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| Perception Test | First/third-person views for object tracking | |
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| Charades-STA | Indoor human actions and interactions | |
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| MoVQA | Diverse scenes for scene transition comprehension | |
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| VLN-CE | Indoor navigation from agent perspective | |
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| TVQA | TV show scenes for episodic reasoning | |
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### Dataset Expansion |
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The original MVBench dataset contains 3,487 videos, which have been systematically expanded through tampering effects, resulting in a total(original+tampered) of **17,435 videos**. This ensures: |
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- **Diversity**: Varied adversarial challenges for robust evaluation. |
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- **Volume**: Sufficient data for training and testing. |
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Below is a visual representation of the tampered video length distribution: |
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 |
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--- |
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## Benchmark Construction |
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MVTamperBench is built with modularity, scalability, and reproducibility at its core: |
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- **Modularity**: Each tampering effect is implemented as a reusable class, allowing for easy adaptation. |
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- **Scalability**: Supports customizable tampering parameters, such as location and duration. |
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- **Integration**: Fully compatible with VLMEvalKit, enabling seamless evaluations of tampering robustness alongside general VLM capabilities. |
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By maintaining consistent tampering duration (1 second) and location (center of the video), MVTamperBench ensures fair and comparable evaluations across models. |
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--- |
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## Download Dataset |
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You can access the MVTamperBenchEnd dataset directly from the Hugging Face repository: |
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[Download MVTamperBenchEnd Dataset](https://huggingface.co/datasets/Srikant86/MVTamperBenchEnd) |
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--- |
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## How to Use |
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1. Clone the Hugging Face repository: |
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```bash |
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git clone [https://huggingface.co/datasets/mvtamperbenchend](https://huggingface.co/datasets/Srikant86/MVTamperBenchEnd) |
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cd mvtamperbench |
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``` |
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2. Load the dataset using the Hugging Face `datasets` library: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("mvtamperbenchend") |
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``` |
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3. Explore the dataset structure and metadata: |
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```python |
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print(dataset["train"]) |
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``` |
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4. Utilize the dataset for tampering detection tasks, model evaluation, and more. |
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--- |
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## Citation |
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If you use MVTamperBench in your research, please cite: |
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```bibtex |
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@misc{agarwal2024mvtamperbenchevaluatingrobustnessvisionlanguage, |
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title={MVTamperBench: Evaluating Robustness of Vision-Language Models}, |
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author={Amit Agarwal and Srikant Panda and Angeline Charles and Bhargava Kumar and Hitesh Patel and Priyanranjan Pattnayak and Taki Hasan Rafi and Tejaswini Kumar and Dong-Kyu Chae}, |
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year={2024}, |
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eprint={2412.19794}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2412.19794}, |
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} |
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``` |
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--- |
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## License |
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MVTamperBench is released under the MIT License. See `LICENSE` for details. |
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