用于事件验证和图像重用检测的深度学习方法
Deep Learning Methods for Event Verification and Image Repurposing Detection
摘要：
社交媒体上发布的图像的真实性是一个越来越令人担忧的问题。已经开发了许多算法来检测被操纵的图像，但是很少有人研究基于深度神经网络的方法来验证图像标签（例如事件名称）的真实性的能力。在本文中，我们提出了几种新的方法来预测图像是否在几个值得注意的事件之一被捕获。我们使用了一组来自几个记录事件的图像，如风暴，马拉松，抗议和其他大型公众集会。提出了两种将预先训练好的Imagenet网络应用于事件验证的策略，并对每种策略进行了两次修改。第一种方法使用来自预训练网络的最后一个卷积层的特征作为分类器的输入。我们还考虑了调整预训练网络的卷积权重以改善分类的影响。第二种方法结合了从较小尺度提取的许多特征，并使用预训练网络的输出作为第二分类器的输入。对于这两种方法，我们研究了几种不同的分类器，并测试了许多不同的预训练网络。我们的实验表明，这两种方法是有效的事件验证和图像重用检测。在全球范围内的分类往往略优于我们测试的本地方法，微调网络进一步改善了结果。
英文摘要：
The authenticity of images posted on social media is an issue of growing concern. Many algorithms have been developed to detect manipulated images, but few have investigated the ability of deep neural network based approaches to verify the authenticity of image labels, such as event names. In this paper, we propose several novel methods to predict if an image was captured at one of several noteworthy events. We use a set of images from several recorded events such as storms, marathons, protests, and other large public gatherings. Two strategies of applying pre-trained Imagenet network for event verification are presented, with two modifications for each strategy. The first method uses the features from the last convolutional layer of a pre-trained network as input to a classifier. We also consider the effects of tuning the convolutional weights of the pre-trained network to improve classification. The second method combines many features extracted from smaller scales and uses the output of a pre-trained network as the input to a second classifier. For both methods, we investigated several different classifiers and tested many different pre-trained networks. Our experiments demonstrate both these approaches are effective for event verification and image re-purposing detection. The classification at the global scale tends to marginally outperform our tested local methods and fine tuning the network further improves the results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
