[2604.08609] Detection of Hate and Threat in Digital Forensics: A Case-Driven Multimodal Approach

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.08609 (cs) [Submitted on 8 Apr 2026] Title:Detection of Hate and Threat in Digital Forensics: A Case-Driven Multimodal Approach Authors:Ponkoj Chandra Shill View a PDF of the paper titled Detection of Hate and Threat in Digital Forensics: A Case-Driven Multimodal Approach, by Ponkoj Chandra Shill View PDF HTML (experimental) Abstract:Digital forensic investigations increasingly rely on heterogeneous evidence such as images, scanned documents, and contextual reports. These artifacts may contain explicit or implicit expressions of harm, hate, threat, violence, or intimidation, yet existing automated approaches often assume clean text input or apply vision models without forensic justification. This paper presents a case-driven multimodal approach for hate and threat detection in forensic analysis. The proposed framework explicitly determines the presence and source of textual evidence, distinguishing between embedded text, associated contextual text, and image-only evidence. Based on the identified evidence configuration, the framework selectively applies text analysis, multimodal fusion, or image-only semantic reasoning using vision language models with vision transformer backbones (ViT). By conditioning inference on evidence availability, the approach mirrors forensic decision-making, improves evidentiary traceability, and avoids unjustified modality assumptions. Experimental evaluation on forensic-style...