TY - GEN
T1 - Differentiating photographic and PRCG images using tampering localization features
AU - Ayyalasomayajula, Roshan Sai
AU - Pankajakshan, Vinod
N1 - Publisher Copyright:
© Springer Science+Business Media Singapore 2017.
PY - 2017
Y1 - 2017
N2 - A large number of sophisticated, yet easily accessible computer graphics softwares (STUDIO MAX, 3D MAYA, etc.) have been developed in the recent past. The images generated with these softwares appear to be realistic and cannot be distinguished from natural images visually. As a result, distinguishing between photographic images (PIM) and Photo-realistic computer generated (PRCG) images of real world objects has become an active area of research. In this paper, we propose that “a computer generated image” would have the features corresponding to a “completely tampered image”, whereas a camera generated picture would not. So, the differentiation is done on the basis of tampering localization features viz., block measure factors based on JPEG compression and re-sampling. It has been observed experimentally, that these measure factors vary for a PIM from a PRCG image. The experimental results show that the proposed simple and robust classifier is able to differentiate between PIM and PRCG images with an accuracy of 96 %.
AB - A large number of sophisticated, yet easily accessible computer graphics softwares (STUDIO MAX, 3D MAYA, etc.) have been developed in the recent past. The images generated with these softwares appear to be realistic and cannot be distinguished from natural images visually. As a result, distinguishing between photographic images (PIM) and Photo-realistic computer generated (PRCG) images of real world objects has become an active area of research. In this paper, we propose that “a computer generated image” would have the features corresponding to a “completely tampered image”, whereas a camera generated picture would not. So, the differentiation is done on the basis of tampering localization features viz., block measure factors based on JPEG compression and re-sampling. It has been observed experimentally, that these measure factors vary for a PIM from a PRCG image. The experimental results show that the proposed simple and robust classifier is able to differentiate between PIM and PRCG images with an accuracy of 96 %.
KW - Image forensics
KW - Photographic images
KW - Photorealistic computer generated images
KW - Steganalysis
KW - Tampering localization
UR - https://www.scopus.com/pages/publications/85009455640
U2 - 10.1007/978-981-10-2107-7_39
DO - 10.1007/978-981-10-2107-7_39
M3 - Conference contribution
AN - SCOPUS:85009455640
SN - 9789811021060
T3 - Advances in Intelligent Systems and Computing
SP - 429
EP - 438
BT - Proceedings of International Conference on Computer Vision and Image Processing, CVIP 2016
A2 - Kumar, Sanjeev
A2 - Raman, Balasubramanian
A2 - Roy, Partha Pratim
A2 - Sen, Debashis
PB - Springer Verlag
T2 - International Conference on Computer Vision and Image Processing, CVIP 2016
Y2 - 26 February 2016 through 28 February 2016
ER -