This study investigates how fake news uses a thumbnail for a news article
with a focus on whether a news article's thumbnail represents the news content
correctly. A news article shared with an irrelevant thumbnail can mislead
readers into having a wrong impression of the issue, especially in social media
environments where users are less likely to click the link and consume the
entire content. We propose to capture the degree of semantic incongruity in the
multimodal relation by using the pretrained CLIP representation. From a
source-level analysis, we found that fake news employs a more incongruous image
to the main content than general news. Going further, we attempted to detect
news articles with image-text incongruity. Evaluation experiments suggest that
CLIP-based methods can successfully detect news articles in which the thumbnail
is semantically irrelevant to news text. This study contributes to the research
by providing a novel view on tackling online fake news and misinformation. Code
and datasets are available at
this https URL.

本研究探讨虚假新闻如何使用新闻文章的缩略图，特别关注新闻文章的缩略图是否正确地代表了新闻内容。我们提出使用预训练的 CLIP 表示来捕捉多模态关系中的语义不一致度，并通过对源头的分析发现，相较于一般新闻，虚假新闻更倾向于使用与主题不相符的图片。我们还尝试检测图文不一致的新闻文章，并通过评估实验表明，基于 CLIP 的方法可以成功识别出缩略图与新闻文本语义不相关的新闻文章。该研究为解决在线虚假新闻和误导信息提供了新视角。

虚假新闻如何使用缩略图？基于 CLIP 的非代表性新闻图像多模态检测

How does fake news use a thumbnail? CLIP-based Multimodal Detection on the Unrepresentative News Image

In this paper, we propose a novel data augmentation strategy named
Cut-Thumbnail, that aims to improve the shape bias of the network. We reduce an
image to a certain size and replace the random region of the original image
with the reduced image. The generated image not only retains most of the
original image information but also has global information in the reduced
image. We call the reduced image as thumbnail. Furthermore, we find that the
idea of thumbnail can be perfectly integrated with Mixed Sample Data
Augmentation, so we put one image's thumbnail on another image while the ground
truth labels are also mixed, making great achievements on various computer
vision tasks. Extensive experiments show that Cut-Thumbnail works better than
state-of-the-art augmentation strategies across classification, fine-grained
image classification, and object detection. On ImageNet classification,
ResNet-50 architecture with our method achieves 79.21\% accuracy, which is more
than 2.8\% improvement on the baseline.

本文提出了一种名为 Cut-Thumbnail 的新型数据增强策略，利用 thumbnail 替代随机区域，可用于提高网络的形状偏差。我们发现通过将 thumbnail 与混合样本数据增强结合，对各种计算机视觉任务进行预测时，该方法的效果优于现有的增强方法。在 ImageNet 分类方面，搭配 ResNet-50 结构使用 Cut-Thumbnail 策略获得了 79.21％的准确率，超过了基线的 2.8％。

Cut-Thumbnail: 一种卷积神经网络的新型数据增强方法

Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural  Network

A large fraction of Internet traffic is now driven by requests from mobile
devices with relatively small screens and often stringent bandwidth
requirements. Due to these factors, it has become the norm for modern
graphics-heavy websites to transmit low-resolution, low-bytecount image
previews (thumbnails) as part of the initial page load process to improve
apparent page responsiveness. Increasing thumbnail compression beyond the
capabilities of existing codecs is therefore a current research focus, as any
byte savings will significantly enhance the experience of mobile device users.
Toward this end, we propose a general framework for variable-rate image
compression and a novel architecture based on convolutional and deconvolutional
LSTM recurrent networks. Our models address the main issues that have prevented
autoencoder neural networks from competing with existing image compression
algorithms: (1) our networks only need to be trained once (not per-image),
regardless of input image dimensions and the desired compression rate; (2) our
networks are progressive, meaning that the more bits are sent, the more
accurate the image reconstruction; and (3) the proposed architecture is at
least as efficient as a standard purpose-trained autoencoder for a given number
of bits. On a large-scale benchmark of 32$\times$32 thumbnails, our LSTM-based
approaches provide better visual quality than (headerless) JPEG, JPEG2000 and
WebP, with a storage size that is reduced by 10% or more.

本文提出了一种基于卷积 LSTM 神经网络的可变比率图像压缩框架和架构，可以用于压缩缩略图以改善移动设备用户的体验，经测试在 32×32 的缩略图上，与 JPEG、JPEG2000 和 WebP 相比，我们的方法在相同存储大小下提供更好的视觉质量。