Open AccessExtended IMD2020: a large‐scale annotated dataset tailored for detecting manipulated images
Author(s) -
Novozámský Adam,
Mahdian Babak,
Saic Stanislav
Publication year - 2021
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/bme2.12025
Subject(s) - computer science , overfitting , artificial intelligence , noise (video) , computer vision , set (abstract data type) , image processing , process (computing) , interpolation (computer graphics) , scale (ratio) , image (mathematics) , pattern recognition (psychology) , artificial neural network , programming language , operating system , physics , quantum mechanics
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.