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 .
@cite_18: This paper provides an overview of the Verifying Multimedia Use task that takes places as part of the 2015 MediaEval Benchmark. The task deals with the automatic detection of manipulation and misuse of Web multimedia content. Its aim is to lay the basis for a future generation of tools that could assist media professionals in the process of verication. Examples of manipulation include maliciously tampering with images and videos, e.g., splicing, removal addition of elements, while other kinds of misuse include the reposting of previously captured multimedia content in a dierent context (e.g., a new event) claiming that it was captured there. For the 2015 edition of the task, we have generated and made available a large corpus of real-world cases of images that were distributed through tweets, along with manually assigned labels regarding their use, i.e. misleading (fake) versus appropriate (real).
@cite_37: 
@cite_33: We propose an approach that predicts whether a tweet, which is accompanied by multimedia content (image video), is trustworthy or deceptive. We test dierent combinations of quality and trust-oriented features (tweet-based, userbased and forensics) in tandem with a standard classication and an agreement-retraining technique, with the goal of predicting the most likely label (fake or real) for each tweet. The experiments carried out on the Verifying Multimedia Use dataset show that the best performance is achieved when using all available features in combination with the agreement-retraining method.
Visual forensics features are generally used for image manipulation detection. To evaluate the authority of attached images, some works extract visual forensics features, such as block artifact grids (BAG), to assist fake news detection. For example, the Verifying Multimedia Use task on 2015 @cite_18 and 2016 MediaEval benchmark @cite_37 provide seven types of image forensics features to help detect manipulated and misleading use of web multimedia content. Based on these provided forensics features, @cite_33 extract advanced forensics features and combine them with post-based and user-based features in a semi-supervised learning scheme to tackle the news verification problem. However, most of these forensics features are crafted manually for detecting specific manipulated traces, which are not applicable for the real images attached in fake news. Besides, these hand-crafted features are labor-expensive and limited to learn complicated patterns, causing the poor generalization performance on the task of fake news detection.