The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: RuntimeError
Message: File 'STFD_ICASSP2023/Readme.md' is encrypted, password required for extraction
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2567, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2103, in __iter__
batch = formatter.format_batch(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 472, in format_batch
batch = self.python_features_decoder.decode_batch(batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 234, in decode_batch
return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2254, in decode_batch
decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1508, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 188, in decode_example
with xopen(path, "rb", download_config=download_config) as f:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 977, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/core.py", line 135, in open
return self.__enter__()
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/core.py", line 103, in __enter__
f = self.fs.open(self.path, mode=mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/spec.py", line 1293, in open
f = self._open(
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/implementations/zip.py", line 129, in _open
out = self.zip.open(path, mode.strip("b"), force_zip64=self.force_zip_64)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1675, in open
raise RuntimeError("File %r is encrypted, password "
RuntimeError: File 'STFD_ICASSP2023/Readme.md' is encrypted, password required for extractionNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
STFD Dataset
This folder contains the STFD (Screenshot Text Forgery Dataset) used in the paper:
Learning to Locate the Text Forgery in Smartphone Screenshots Zeqin Yu, Bin Li, Yuzhen Lin, Jinhua Zeng, Jishen Zeng ICASSP 2023
Project page: https://github.com/ZeqinYu/STFL-Net
The STFD dataset has been publicly released (Google Drive / Baidu Netdisk (dwmg) / Huggingface). Please send an email to [email protected] using your academic or institutional email address to request the password for extracting the dataset.
The email should include:
- Your name, affiliation, and homepage (if available)
- Your supervisor's name, affiliation, and homepage (if available)
- A brief description of your research purpose
STFD is designed for text image forgery localization (T-IFL) in smartphone screenshots. The dataset contains tampered screenshots and their corresponding pixel-level forgery masks.
Screenshot Sources
The screenshots were captured from real devices to reflect realistic usage scenarios.
| Category | Description |
|---|---|
| Systems | Android, HarmonyOS, iOS, Windows |
| Scenes | Chat, Social Media, Mobile Payment, E-commerce, Online Banking, Maps & Transportation, Web Browsing, System Interfaces, Documents |
| Devices | Realme Q3 Pro, Oppo Reno1, Honor 9, Honor V30, Vivo X21s, Samsung Note20 Ultra, Vivo X60, Honor 30-1, Xiaomi 9, Honor V20, Nova 8, OnePlus 9, Huawei Mate30, Honor 30-2, Honor 20 Pro, iPhone 7, iPad Air 3, iPad 2020, iPhone 12, iPhone XS, iPhone 11, iPhone SE2, iPhone 14 Pro, MacBook Air 2015, MacBook Pro 2017, Win10 Dell Optiplex 7080, Win11 Xiaomi Air14, Win10 Xiaomi Air14 |
| Format | PNG / JPEG |
| Tampering Types | Copy-Move, Splicing, Removal, Insertion, Replacement |
Tampering Examples
Copy-Move
Copy a text region and paste it to another location within the same image.
| Example1 | Example2 |
|---|---|
![]() |
![]() |
Splicing
Paste text regions from another image into the target image.
| Example1 | Example2 |
|---|---|
![]() |
![]() |
Removal
Remove existing text and fill the region using inpainting.
| Example1 | Example2 |
|---|---|
![]() |
![]() |
Insertion
Insert new text content into blank regions.
| Example1 | Example2 |
|---|---|
![]() |
![]() |
Replacement
Replace original text with newly generated text.
| Example1 | Example2 |
|---|---|
![]() |
![]() |
Dataset Structure
STFD/
βββ 1_Copy-move/
β βββ tamper/ # tampered screenshots
β βββ masks/ # binary forgery masks
βββ 2_Splicing/
β βββ tamper/
β βββ masks/
βββ 3_Removal/
β βββ tamper/
β βββ masks/
βββ 4_Insertion/
β βββ tamper/
β βββ masks/
βββ 5_Replacement/
βββ tamper/
βββ masks/
Each image in tamper/ has a corresponding mask with the same filename in masks/.
Example:
tamper/ffa3cedd4317633601c6fb82d94fc783.png
masks/ffa3cedd4317633601c6fb82d94fc783.png
The mask is a binary image, where:
- 0 = non-tampered region
- 255 = tampered region
Citation
If you use this dataset in your research, please cite:
@inproceedings{yu2023learning,
title = {Learning to Locate the Text Forgery in Smartphone Screenshots},
author = {Yu, Zeqin and Li, Bin and Lin, Yuzhen and Zeng, Jinhua and Zeng, Jishen},
booktitle = {ICASSP 2023β2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {1--5},
year = {2023},
organization = {IEEE}
}
License and Notice
This dataset is released for academic research purposes only.
All images are collected from real-world scenarios and have been manually screened to remove sensitive information.
If you believe that any image may contain unintended information leakage, please notify us so that we can remove it promptly. We kindly ask users not to redistribute such images.
For any concerns, please contact: Zeqin Yu ([email protected]).
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