利用语义分割增强地面到航空图像匹配以检测视觉虚假信息
Enhancing Ground-to-Aerial Image Matching for Visual Misinformation Detection Using Semantic Segmentation
AI总结：
本研究提出四流孪生式架构SAN-QUAD，利用语义分割增强地面与卫星图像匹配，在CVUSA子集上实现高达9.8%的精度提升，用于视觉虚假信息检测中的地理定位。
摘要：
生成式人工智能技术的最新进展显著增加了篡改图像和视频的在线传播，这引发了人们对互联网上以及通过信息渠道和社交网络分发的数字媒体可信度的严重担忧。这一问题尤其影响严重依赖可信数据的领域，如新闻业、法医分析和地球观测。为应对这些担忧，在没有GPS坐标等外部信息的情况下对非地理标记的地面视角图像进行地理定位的能力变得日益关键。本研究解决了在没有GPS数据辅助的情况下，将可能呈现不同视场（FoV）的地面视角图像与其对应的卫星图像关联起来的挑战。为实现这一目标，我们提出了一种新颖的四流孪生式架构——四重语义对齐网络（SAN-QUAD），该架构通过利用应用于地面和卫星图像的语义分割，扩展了先前最先进（SOTA）方法。在CVUSA数据集子集上的实验结果表明，在各种FoV设置下，相较于先前方法取得了高达9.8%的显著改进。
英文摘要：
The recent advancements in generative AI techniques, which have significantly increased the online dissemination of altered images and videos, have raised serious concerns about the credibility of digital media available on the Internet and distributed through information channels and social networks. This issue particularly affects domains that rely heavily on trustworthy data, such as journalism, forensic analysis, and Earth observation. To address these concerns, the ability to geolocate a non-geo-tagged ground-view image without external information, such as GPS coordinates, has become increasingly critical. This study tackles the challenge of linking a ground-view image, potentially exhibiting varying fields of view (FoV), to its corresponding satellite image without the aid of GPS data. To achieve this, we propose a novel four-stream Siamese-like architecture, the Quadruple Semantic Align Net (SAN-QUAD), which extends previous state-of-the-art (SOTA) approaches by leveraging semantic segmentation applied to both ground and satellite imagery. Experimental results on a subset of the CVUSA dataset demonstrate significant improvements of up to 9.8% over prior methods across various FoV settings.
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