  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.
