  The increasing popularity of social media promotes the proliferation of fake
news. With the development of multimedia technology, fake news attempts to
utilize multimedia contents with images or videos to attract and mislead
readers for rapid dissemination, which makes visual contents an important part
of fake news. Fake-news images, images attached in fake news posts,include not
only fake images which are maliciously tampered but also real images which are
wrongly used to represent irrelevant events. Hence, how to fully exploit the
inherent characteristics of fake-news images is an important but challenging
problem for fake news detection. In the real world, fake-news images may have
significantly different characteristics from real-news images at both physical
and semantic levels, which can be clearly reflected in the frequency and pixel
domain, respectively. Therefore, we propose a novel framework Multi-domain
Visual Neural Network (MVNN) to fuse the visual information of frequency and
pixel domains for detecting fake news. Specifically, we design a CNN-based
network to automatically capture the complex patterns of fake-news images in
the frequency domain; and utilize a multi-branch CNN-RNN model to extract
visual features from different semantic levels in the pixel domain. An
attention mechanism is utilized to fuse the feature representations of
frequency and pixel domains dynamically. Extensive experiments conducted on a
real-world dataset demonstrate that MVNN outperforms existing methods with at
least 9.2% in accuracy, and can help improve the performance of multimodal fake
news detection by over 5.2%.
