  In this paper, we propose a simple yet effective solution to a change
detection task that detects the difference between two images, which we call
"spot the difference". Our approach uses CNN-based object detection by stacking
two aligned images as input and considering the differences between the two
images as objects to detect. An early-merging architecture is used as the
backbone network. Our method is accurate, fast and robust while using very
cheap annotation. We verify the proposed method on the task of change detection
between the digital design and its photographic image of a book. Compared to
verification based methods, our object detection based method outperforms other
methods by a large margin and gives extra information of location. We compress
the network and achieve 24 times acceleration while keeping the accuracy.
Besides, as we synthesize the training data for detection using weakly labeled
images, our method does not need expensive bounding box annotation.
