Exploiting Multidomain Visual Information for Fake News Detection The increasing popularity of social media promotes the proliferation of fake news. With the development of multimedia technology, fake news attempts to utilize multimedia content with images or videos to attract and mislead readers for rapid dissemination, which makes visual content an important part of fake news. Fakenews images, images attached to fake news posts, include not only fake images that are maliciously tampered but also real images that are wrongly used to represent irrelevant events. Hence, how to fully exploit the inherent characteristics of fakenews images is an important but challenging problem for fake news detection. In the real world, fakenews images may have significantly different characteristics from realnews 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 Multidomain Visual Neural Network (MVNN) to fuse the visual information of frequency and pixel domains for detecting fake news. Specifically, we design a CNNbased network to automatically capture the complex patterns of fakenews images in the frequency domain; and utilize a multibranch CNNRNN 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%.

The Impact of Domain Randomization on Object Detection: A Case Study on Parametric Shapes and Synthetic Textures * Recent advances in deep learningxe2x80x93based object detection techniques have revolutionized their applicability in several fields. However, since these methods rely on unwieldy and large amounts of data, a common practice is to download models pretrained on standard datasets and finetune them for specific application domains with a small set of domainxe2x80x93relevant images. In this work, we show that using synthetic datasets that are not necessarily photorealistic can be a better alternative to simply finetune pretrained networks. Specifically, our results show an impressive 25%improvement in the mAP metric over a finetuning baseline when only about 200 labelled images are available to train. Finally, an ablation study of our results is presented to delineate the individual contribution of different components in the randomization pipeline.