| dc.description.abstract | With recent developments in information technology and artificial intelligence (AI), image-based insurance claims
forgeries are now easier than ever to produce. Fundamental to an effective detection of such fraudulent insurance
claims is the development of AI-models for automatic processing of claim-specific image data for the purpose
of forgery detection. Although general forgery detection models are being developed, and machine learning
methods are becoming increasingly popular in insurance fraud detection, practical applications of computer
vision techniques to claim-specific image data are sparse. Here, we adapt the recently proposed Hierarchical
Fine-Grained Image Forgery Detector and Localizer (HiFi-Net), for practical application in insurance image
forgery detection and show that it can accurately detect image forgeries in natural images from the real-world
car insurance claims domain. Furthermore, we report that the integration of a Vision-Transformer (ViT)-based
masked auto-encoder into the network reduces forgery detection performance, due to the difference in pre
training objectives. Application of our network to a document-type forgery detection task reveals cross-domain
generalizability. The addition of a textual feature extractor slightly increased model performance, highlighting
the strength of hybrid vision-textual architectures. However, it struggles to detect real-world document-type
manipulations, mainly because of domain shifts and differences in forgery attributes between datasets. Lastly, we
construct a hierarchical car-based insurance image forgery detection dataset to facilitate our study. This study
provides us with more insight into forgery detection networks and how a single model can detect various types
of image forgeries. Moreover, it highlights the importance of targeted fine-tuning in addressing domain-specific
challenges, ensuring that AI-driven forgery detection remains effective and reliable for real-world insurance ap
plications. | |
