  Numerous fake images spread on social media today and can severely jeopardize
the credibility of online content to public. In this paper, we employ deep
networks to learn distinct fake image related features. In contrast to
authentic images, fake images tend to be eye-catching and visually striking.
Compared with traditional visual recognition tasks, it is extremely challenging
to understand these psychologically triggered visual patterns in fake images.
Traditional general image classification datasets, such as ImageNet set, are
designed for feature learning at the object level but are not suitable for
learning the hyper-features that would be required by image credibility
analysis. In order to overcome the scarcity of training samples of fake images,
we first construct a large-scale auxiliary dataset indirectly related to this
task. This auxiliary dataset contains 0.6 million weakly-labeled fake and real
images collected automatically from social media. Through an AdaBoost-like
transfer learning algorithm, we train a CNN model with a few instances in the
target training set and 0.6 million images in the collected auxiliary set. This
learning algorithm is able to leverage knowledge from the auxiliary set and
gradually transfer it to the target task. Experiments on a real-world testing
set show that our proposed domain transferred CNN model outperforms several
competing baselines. It obtains superiror results over transfer learning
methods based on the general ImageNet set. Moreover, case studies show that our
proposed method reveals some interesting patterns for distinguishing fake and
authentic images.
