  We present a novel approach for inspecting variable data prints (VDP) with an
ultra-low false alarm rate (0.005%) and potential applicability to other
real-world problems. The system is based on a comparison between two images: a
reference image and an image captured by low-cost scanners. The comparison task
is challenging as low-cost imaging systems create artifacts that may
erroneously be classified as true (genuine) defects. To address this challenge
we introduce two new fusion methods, for change detection applications, which
are both fast and efficient. The first is an early fusion method that combines
the two input images into a single pseudo-color image. The second, called
Change-Detection Single Shot Detector (CD-SSD) leverages the SSD by fusing
features in the middle of the network. We demonstrate the effectiveness of the
proposed deep learning-based approach with a large dataset from real-world
printing scenarios. Finally, we evaluate our models on a different domain of
aerial imagery change detection (AICD). Our best method clearly outperforms the
state-of-the-art baseline on this dataset.
