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

Researchers present a forensic-focused multimodal framework for detecting hate speech and threats across images, documents, and text. The approach intelligently determines what evidence is present before applying appropriate AI models, improving accuracy and evidentiary traceability in digital investigations.

This research addresses a critical gap in digital forensics by acknowledging that real-world evidence rarely arrives in clean, uniform formats. Traditional hate and threat detection systems assume structured text input or blindly apply vision models without considering what evidence actually exists in a case. The proposed framework inverts this logic by first determining evidence configuration—whether text is embedded in images, associated contextually, or absent entirely—then conditionally applying the appropriate detection methodology.

The approach builds on established vision-language models and vision transformers but introduces forensic discipline to their application. By explicitly documenting which modalities informed a detection decision, investigators gain defensible reasoning suitable for legal proceedings. This mirrors how human analysts actually work: they assess what evidence is available before choosing analysis methods.

For the broader forensic and law enforcement sector, this framework addresses a persistent challenge in digital investigations where heterogeneous evidence types complicate automated analysis pipelines. Courts increasingly scrutinize the justification for AI-derived conclusions, making this evidence-aware approach more legally sound than black-box multimodal systems.

The experimental validation on forensic-style image evidence demonstrates the framework produces consistent results across varied evidence scenarios, suggesting practical deployment readiness. As digital evidence becomes central to criminal investigations worldwide, tools that improve both accuracy and interpretability hold significant value for law enforcement agencies and forensic specialists.

• →Framework determines evidence type before applying detection models, improving accuracy and legal defensibility.
• →Addresses the reality that forensic evidence combines images, scanned documents, and contextual text rather than clean inputs.
• →Uses vision transformers and vision-language models with conditional inference based on available evidence.
• →Provides explicit evidentiary traceability required for courtroom admissibility of AI-derived conclusions.
• →Experimental results show consistent behavior across heterogeneous evidence configurations in forensic contexts.