| bibtype |
J -
Journal Article
|
| ARLID |
0541341 |
| utime |
20240103225634.5 |
| mtime |
20210324235959.9 |
| SCOPUS |
85122093887 |
| WOS |
000631767900001 |
| DOI |
10.1049/bme2.12025 |
| title
(primary) (eng) |
Extended IMD2020: a large‐scale annotated dataset tailored for detecting manipulated images |
| specification |
| page_count |
16 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0445548 |
| ISSN |
2047-4938 |
| title
|
IET Biometrics |
| volume_id |
10 |
| volume |
4 (2021) |
| page_num |
392-407 |
| publisher |
|
|
| keyword |
image manipulation |
| keyword |
image forensics |
| keyword |
database |
| keyword |
tampering |
| author
(primary) |
| ARLID |
cav_un_auth*0283562 |
| name1 |
Novozámský |
| name2 |
Adam |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept (eng) |
Department of Image Processing |
| department (cz) |
ZOI |
| department (eng) |
ZOI |
| full_dept |
Department of Image Processing |
| garant |
K |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0206076 |
| name1 |
Mahdian |
| name2 |
Babak |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept |
Department of Image Processing |
| department (cz) |
ZOI |
| department |
ZOI |
| full_dept |
Department of Image Processing |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101189 |
| name1 |
Saic |
| name2 |
Stanislav |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept |
Department of Image Processing |
| department (cz) |
ZOI |
| department |
ZOI |
| full_dept |
Department of Image Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
825227 |
| agency |
EC |
| country |
XE |
| ARLID |
cav_un_auth*0392648 |
|
| abstract
(eng) |
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. |
| result_subspec |
WOS |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2022 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0319273 |
| confidential |
S |
| mrcbC86 |
3+4 Article Computer Science Artificial Intelligence |
| mrcbC91 |
A |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-j |
0.51 |
| mrcbT16-s |
0.951 |
| mrcbT16-D |
Q4 |
| mrcbT16-E |
Q2 |
| arlyear |
2021 |
| mrcbU14 |
85122093887 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000631767900001 WOS |
| mrcbU63 |
cav_un_epca*0445548 IET Biometrics 2047-4938 2047-4946 Roč. 10 č. 4 2021 392 407 Wiley |
|