Detection of Hate and Threat in Digital Forensics: A Case-Driven Multimodal Approach
For digital forensic investigators, this work provides a more interpretable and evidence-aware method for detecting harmful content in heterogeneous forensic artifacts, though it is incremental in nature.
The paper presents a case-driven multimodal framework for hate and threat detection in digital forensics that adapts its analysis based on evidence type (embedded text, contextual text, or image-only). The approach improves evidentiary traceability and avoids unjustified modality assumptions, with consistent performance across heterogeneous forensic evidence scenarios.
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 image evidence demonstrates consistent and interpretable behavior across heterogeneous evidence scenarios.