readme added

README.md

@@ -0,0 +1,182 @@
+---
+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: fine_grained_pose
+  data_files: json/fine_grained_pose.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
+---
+# 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:
+
+1. **Masking**: Overlays a black rectangle on a 1-second segment, simulating visual data loss.
+2. **Repetition**: Repeats a 1-second segment, introducing temporal redundancy.
+3. **Rotation**: Rotates a 1-second segment by 180 degrees, introducing spatial distortion.
+4. **Substitution**: Replaces a 1-second segment with a random clip from another video, disrupting the temporal and contextual flow.
+
+The tampering effects are applied to the middle of each video to ensure consistent evaluation across models.
+
+---
+
+## Dataset Details
+
+The MVTamperBench 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:
+
+- **Content Diversity**: Spanning a variety of objects, activities, and settings.
+- **Temporal Dynamics**: Videos with temporal dependencies for coherence testing.
+- **Benchmark Utility**: Recognized datasets enabling comparisons with prior work.
+
+### Incorporated Datasets
+
+The MVTamperBench dataset integrates videos from several sources, each contributing unique characteristics:
+
+| Dataset Name         | Primary Scene Type and Unique Characteristics                          |
+|----------------------|-------------------------------------------------------------------------|
+| STAR      | Indoor actions and object interactions                               |
+| PAXION    | Real-world scenes with nuanced actions                              |
+| Moments in Time (MiT) V1 | Indoor/outdoor scenes across varied contexts            |
+| FunQA     | Humor-focused, creative, real-world events                          |
+| CLEVRER   | Simulated scenes for object movement and reasoning                  |
+| Perception Test  | First/third-person views for object tracking                    |
+| Charades-STA   | Indoor human actions and interactions                              |
+| MoVQA     | Diverse scenes for scene transition comprehension                   |
+| VLN-CE       | Indoor navigation from agent perspective                            |
+| TVQA     | TV show scenes for episodic reasoning                               |
+
+### Dataset Expansion
+
+The original MVBench dataset contains 3,699 videos, which have been systematically expanded through tampering effects, resulting in a total of **18,495 videos**. This ensures:
+
+- **Diversity**: Varied adversarial challenges for robust evaluation.
+- **Volume**: Sufficient data for training and testing.
+
+Below is a visual representation of the tampered video length distribution:
+
+![Tampered Video Length Distribution](./assert/tampered_video_length_distribution.png "Distribution of tampered video lengths")
+
+---
+
+## Benchmark Construction
+
+MVTamperBench is built with modularity, scalability, and reproducibility at its core:
+
+- **Modularity**: Each tampering effect is implemented as a reusable class, allowing for easy adaptation.
+- **Scalability**: Supports customizable tampering parameters, such as location and duration.
+- **Integration**: Fully compatible with VLMEvalKit, enabling seamless evaluations of tampering robustness alongside general VLM capabilities.
+
+By maintaining consistent tampering duration (1 second) and location (center of the video), MVTamperBench ensures fair and comparable evaluations across models.
+
+---
+
+## Download Dataset
+
+You can access the MVTamperBench dataset directly from the Hugging Face repository:
+
+[Download MVTamperBench Dataset](https://huggingface.co/datasets/Srikant86/MVTamperBench)
+
+---
+
+## How to Use
+
+1. Clone the Hugging Face repository:
+   ```bash
+   git clone [https://huggingface.co/datasets/mvtamperbenchstart](https://huggingface.co/datasets/Srikant86/MVTamperBenchEnd)
+   cd mvtamperbench
+   ```
+
+2. Load the dataset using the Hugging Face `datasets` library:
+   ```python
+   from datasets import load_dataset
+
+   dataset = load_dataset("mvtamperbench")
+   ```
+
+3. Explore the dataset structure and metadata:
+   ```python
+   print(dataset["train"])
+   ```
+
+4. Utilize the dataset for tampering detection tasks, model evaluation, and more.
+
+---
+
+## Citation
+
+If you use MVTamperBench in your research, please cite:
+
+```bibtex
+@misc{agarwal2024mvtamperbenchevaluatingrobustnessvisionlanguage,
+      title={MVTamperBench: Evaluating Robustness of Vision-Language Models},
+      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},
+      year={2024},
+      eprint={2412.19794},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2412.19794},
+}
+```
+
+---
+
+## License
+
+MVTamperBench is released under the MIT License. See `LICENSE` for details.