  The authenticity of images posted on social media is an issue of growing
concern. Many algorithms have been developed to detect manipulated images, but
few have investigated the ability of deep neural network based approaches to
verify the authenticity of image labels, such as event names. In this paper, we
propose several novel methods to predict if an image was captured at one of
several noteworthy events. We use a set of images from several recorded events
such as storms, marathons, protests, and other large public gatherings. Two
strategies of applying pre-trained Imagenet network for event verification are
presented, with two modifications for each strategy. The first method uses the
features from the last convolutional layer of a pre-trained network as input to
a classifier. We also consider the effects of tuning the convolutional weights
of the pre-trained network to improve classification. The second method
combines many features extracted from smaller scales and uses the output of a
pre-trained network as the input to a second classifier. For both methods, we
investigated several different classifiers and tested many different
pre-trained networks. Our experiments demonstrate both these approaches are
effective for event verification and image re-purposing detection. The
classification at the global scale tends to marginally outperform our tested
local methods and fine tuning the network further improves the results.
