TY - GEN
T1 - Content authentication for neural imaging pipelines
T2 - 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
AU - Korus, Pawel
AU - Memon, Nasir
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Forensic analysis of digital photo provenance relies on intrinsic traces left in the photograph at the time of its acquisition. Such analysis becomes unreliable after heavy post-processing, such as down-sampling and re-compression applied upon distribution in the Web. This paper explores end-to-end optimization of the entire image acquisition and distribution workflow to facilitate reliable forensic analysis at the end of the distribution channel. We demonstrate that neural imaging pipelines can be trained to replace the internals of digital cameras, and jointly optimized for high-fidelity photo development and reliable provenance analysis. In our experiments, the proposed approach increased image manipulation detection accuracy from 45% to over 90%. The findings encourage further research towards building more reliable imaging pipelines with explicit provenance-guaranteeing properties.
AB - Forensic analysis of digital photo provenance relies on intrinsic traces left in the photograph at the time of its acquisition. Such analysis becomes unreliable after heavy post-processing, such as down-sampling and re-compression applied upon distribution in the Web. This paper explores end-to-end optimization of the entire image acquisition and distribution workflow to facilitate reliable forensic analysis at the end of the distribution channel. We demonstrate that neural imaging pipelines can be trained to replace the internals of digital cameras, and jointly optimized for high-fidelity photo development and reliable provenance analysis. In our experiments, the proposed approach increased image manipulation detection accuracy from 45% to over 90%. The findings encourage further research towards building more reliable imaging pipelines with explicit provenance-guaranteeing properties.
KW - Low-level Vision
KW - Vision Applications and Systems
UR - http://www.scopus.com/inward/record.url?scp=85078731104&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078731104&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2019.00882
DO - 10.1109/CVPR.2019.00882
M3 - Conference contribution
AN - SCOPUS:85078731104
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 8613
EP - 8621
BT - Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
PB - IEEE Computer Society
Y2 - 16 June 2019 through 20 June 2019
ER -