Multimodal Models Meet Presentation Attack Detection on ID Documents
This addresses biometric security for ID verification systems, but it is incremental as it builds on existing multimodal approaches with limited success.
This study tackled the problem of detecting presentation attacks on ID documents by integrating multimodal models, but the results showed that these models struggled to accurately detect such attacks.
The integration of multimodal models into Presentation Attack Detection (PAD) for ID Documents represents a significant advancement in biometric security. Traditional PAD systems rely solely on visual features, which often fail to detect sophisticated spoofing attacks. This study explores the combination of visual and textual modalities by utilizing pre-trained multimodal models, such as Paligemma, Llava, and Qwen, to enhance the detection of presentation attacks on ID Documents. This approach merges deep visual embeddings with contextual metadata (e.g., document type, issuer, and date). However, experimental results indicate that these models struggle to accurately detect PAD on ID Documents.