Extended IMD2020: a large‐scale annotated dataset tailored for detecting manipulated images
Abstract Image forensic datasets need to accommodate a complex diversity of systematic noise and intrinsic image artefacts to prevent any overfitting of learning methods to a small set of camera types or manipulation techniques. Such artefacts are created during the image acquisition as well as the...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-07-01
|
| Series: | IET Biometrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/bme2.12025 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546711618191360 |
|---|---|
| author | Adam Novozámský Babak Mahdian Stanislav Saic |
| author_facet | Adam Novozámský Babak Mahdian Stanislav Saic |
| author_sort | Adam Novozámský |
| collection | DOAJ |
| description | Abstract Image forensic datasets need to accommodate a complex diversity of systematic noise and intrinsic image artefacts to prevent any overfitting of learning methods to a small set of camera types or manipulation techniques. Such artefacts are created during the image acquisition as well as the manipulating process itself (e.g., noise due to sensors, interpolation artefacts, etc.). Here, the authors introduce three datasets. First, we identified the majority of camera models on the market. Then, we collected a dataset of 35,000 real images captured by these cameras. We also created the same number of digitally manipulated images. Additionally, we also collected a dataset of 2,000 ‘real‐life’ (uncontrolled) manipulated images. They are made by unknown people and downloaded from the Internet. The real versions of these images are also provided. We also manually created binary masks localising the exact manipulated areas of these images. Moreover, we captured a set of 2,759 real images formed by 32 unique cameras (19 different camera models) in a controlled way by ourselves. Here, the processing history of all images is guaranteed. This set includes categorised images of uniform areas as well as natural images that can be used effectively for analysis of the sensor noise. |
| format | Article |
| id | doaj-art-0258d30fe71348df9cdf1972c2f3d1b4 |
| institution | Kabale University |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2021-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-0258d30fe71348df9cdf1972c2f3d1b42025-02-03T06:47:18ZengWileyIET Biometrics2047-49382047-49462021-07-0110439240710.1049/bme2.12025Extended IMD2020: a large‐scale annotated dataset tailored for detecting manipulated imagesAdam Novozámský0Babak Mahdian1Stanislav Saic2The Czech Academy of Sciences Institute of Information Theory and Automation Prague, CzechiaThe Czech Academy of Sciences Institute of Information Theory and Automation Prague, CzechiaThe Czech Academy of Sciences Institute of Information Theory and Automation Prague, CzechiaAbstract Image forensic datasets need to accommodate a complex diversity of systematic noise and intrinsic image artefacts to prevent any overfitting of learning methods to a small set of camera types or manipulation techniques. Such artefacts are created during the image acquisition as well as the manipulating process itself (e.g., noise due to sensors, interpolation artefacts, etc.). Here, the authors introduce three datasets. First, we identified the majority of camera models on the market. Then, we collected a dataset of 35,000 real images captured by these cameras. We also created the same number of digitally manipulated images. Additionally, we also collected a dataset of 2,000 ‘real‐life’ (uncontrolled) manipulated images. They are made by unknown people and downloaded from the Internet. The real versions of these images are also provided. We also manually created binary masks localising the exact manipulated areas of these images. Moreover, we captured a set of 2,759 real images formed by 32 unique cameras (19 different camera models) in a controlled way by ourselves. Here, the processing history of all images is guaranteed. This set includes categorised images of uniform areas as well as natural images that can be used effectively for analysis of the sensor noise.https://doi.org/10.1049/bme2.12025camerasimage classificationimage codingimage colour analysisimage processingimage representation |
| spellingShingle | Adam Novozámský Babak Mahdian Stanislav Saic Extended IMD2020: a large‐scale annotated dataset tailored for detecting manipulated images IET Biometrics cameras image classification image coding image colour analysis image processing image representation |
| title | Extended IMD2020: a large‐scale annotated dataset tailored for detecting manipulated images |
| title_full | Extended IMD2020: a large‐scale annotated dataset tailored for detecting manipulated images |
| title_fullStr | Extended IMD2020: a large‐scale annotated dataset tailored for detecting manipulated images |
| title_full_unstemmed | Extended IMD2020: a large‐scale annotated dataset tailored for detecting manipulated images |
| title_short | Extended IMD2020: a large‐scale annotated dataset tailored for detecting manipulated images |
| title_sort | extended imd2020 a large scale annotated dataset tailored for detecting manipulated images |
| topic | cameras image classification image coding image colour analysis image processing image representation |
| url | https://doi.org/10.1049/bme2.12025 |
| work_keys_str_mv | AT adamnovozamsky extendedimd2020alargescaleannotateddatasettailoredfordetectingmanipulatedimages AT babakmahdian extendedimd2020alargescaleannotateddatasettailoredfordetectingmanipulatedimages AT stanislavsaic extendedimd2020alargescaleannotateddatasettailoredfordetectingmanipulatedimages |