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README.md

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-# 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.
+# MVTamperBench Dataset
 
-## 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
+## Overview
 
-### 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.
+**MVTamperBench** 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 five distinct tampering techniques:
+
+1. **Frame Dropping**: Removes a 1-second segment, creating temporal discontinuity.
+2. **Masking**: Overlays a black rectangle on a 1-second segment, simulating visual data loss.
+3. **Repetition**: Repeats a 1-second segment, introducing temporal redundancy.
+4. **Rotation**: Rotates a 1-second segment by 180 degrees, introducing spatial distortion.
+5. **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](path/to/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/mvtamperbench](https://huggingface.co/datasets/Srikant86/MVTamperBench)
+   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.