TY - GEN
T1 - CamCarv - Expose the Source Camera at the Rear of Seam Insertion
AU - Irshad, Muhammad
AU - Law, Ngai Fong
AU - Loo, Ka Hong
N1 - Funding Information:
Acknowledgement. This work was supported by the GRF Grant 15211720, (project code: Q79N), of the Hong Kong SAR Government. Muhammad Irshad Ibrahim would like to thank the postdoctoral fellowship support from the Hong Kong Polytechnic University (GYW4X).
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/1
Y1 - 2023/1
N2 - It is well known that photo response non-uniformity (PRNU) noise based source attribution helps to verify the camera used to take an image. Recent advances in content-aware image resizing method such as seam carving allow an image to be resized while the critical content is retained. In this paper, we propose identifying the source camera from seam inserted images using blocks as small as 20 × 20. In particular, the correlation is computed between the noise residue of the seam inserted image and the camera PRNU constructed using different numbers of im-ages. We found that different correlation patterns with the camera PRNUs are ob-served, depending on whether the image is taken by that camera or not. Addition-ally, based on this observation, features are extracted from the correlation patterns which are then weighted and combined to form a decision metric for source cam-era identification. We demonstrate by our experimental results that our approach is effective in identifying the source camera in seam insertion images.
AB - It is well known that photo response non-uniformity (PRNU) noise based source attribution helps to verify the camera used to take an image. Recent advances in content-aware image resizing method such as seam carving allow an image to be resized while the critical content is retained. In this paper, we propose identifying the source camera from seam inserted images using blocks as small as 20 × 20. In particular, the correlation is computed between the noise residue of the seam inserted image and the camera PRNU constructed using different numbers of im-ages. We found that different correlation patterns with the camera PRNUs are ob-served, depending on whether the image is taken by that camera or not. Addition-ally, based on this observation, features are extracted from the correlation patterns which are then weighted and combined to form a decision metric for source cam-era identification. We demonstrate by our experimental results that our approach is effective in identifying the source camera in seam insertion images.
KW - Correlation pattern
KW - Seam insertion
KW - Source camera identification
UR - http://www.scopus.com/inward/record.url?scp=85149185753&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-23480-4_2
DO - 10.1007/978-3-031-23480-4_2
M3 - Conference article published in proceeding or book
AN - SCOPUS:85149185753
SN - 9783031234798
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 21
EP - 34
BT - Artificial Intelligence and Soft Computing - 21st International Conference, ICAISC 2022, Proceedings
A2 - Rutkowski, Leszek
A2 - Rutkowski, Leszek
A2 - Scherer, Rafał
A2 - Korytkowski, Marcin
A2 - Pedrycz, Witold
A2 - Tadeusiewicz, Ryszard
A2 - Zurada, Jacek M.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Artificial Intelligence and Soft Computing, ICAISC 2022
Y2 - 19 June 2022 through 23 June 2022
ER -