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--- |
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task_categories: |
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- object-detection |
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tags: |
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- watermak |
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- computer-vision |
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- object-detection |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: "data/train.zip" |
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- split: test |
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path: "data/test.zip" |
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- split: val |
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path: "data/val.zip" |
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--- |
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# Visible watermarks datasets |
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We have observed that while datasets such as COCO are available for object detection, the availability of datasets |
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specifically designed for the detection of watermarks added to images is significantly limited. Through our research, |
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we identified only one such dataset, which originates from the paper Wdnet: Watermark-Decomposition Network for |
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Visible Watermark Removal [1]. This dataset provides a collection of images along with their corresponding watermark |
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masks for the purpose of watermark removal. Additionally, we noted that accessing this dataset presented challenges in |
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terms of data accessibility and regeneration of dataset samples. |
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The CLWD Dataset, introduced in Wdnet: Watermark-Decomposition Network for Visible Watermark Removal [1], |
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comprises images sourced from the COCO Dataset (Lin et al., 2014) [ 2] and masks of colored watermarks featuring |
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random positions and opacities |
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## Dataset Details (PITA Dataset) |
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We decided to introduce the Pita dataset, which is based on images from the COCO dataset (Lin et al., 2014) [ 2] and |
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combines these with logos from the Open Logo Detection Challenge (Su et al., 2018) [3]. |
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The dataset introduces several changes compared to other datasets, with a focus on the task of watermark detection |
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rather than watermark removal. |
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The dataset is structured into three splits: a training split, a validation split, and a test split, collectively comprising |
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approximately 20 000 watermarked images featuring both logos and text. |
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We decided to incorporate two types of labels: |
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- Text: The images are watermarked with a random font available on the computer used for generation, and the |
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text size is also randomized. |
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- Logos: The logos are sourced from the Open Logo Detection Challenge dataset (Su et al., 2018) and are |
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characterized by random sizes and opacities. |
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The position of the logo or text is randomly selected from a set of available positions, specifically corners or the center. |
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This restriction was introduced based on the observation that watermarks on social media or stock image websites are |
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predominantly located in these positions. |
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The dataset is accompanied by command-line interface tools that facilitate reproducibility. These tools support both |
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YOLO and Hugging Face formats, allowing the download of the dataset and generation with ease |
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### Dataset Sources |
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- **Repository:** https://github.com/OrdinaryDev83/dnn-watermark |
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- **Demo:** https://huggingface.co/spaces/qfisch/watermark-detection |
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## Uses |
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- DETR with Hugging face Transformers |
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- YoloV8 model with ultralytics |
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- FastRCNN with Pytorch Lighthning |
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### Source Data |
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- COCO dataset (Lin et al., 2014) [ 2] and |
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- Open Logo Detection Challenge (Su et al., 2018) [3]. |
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#### Data Collection and Processing |
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Generation of the dataset is **reproducible** using the cli tool of this [repository](https://github.com/OrdinaryDev83/dnn-watermark). |
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A --help option is available in order to describe how to use the tool. |
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## Annotation process |
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Logo were added to COCO images by applying **rotation**, **scaling** and **opacity** changes at a random position on the image. |
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on the image. |
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