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

A new research paper titled 'Detection of Hate and Threat in Digital Forensics: A Case-Driven Multimodal Approach' introduces an AI framework specifically designed for forensic investigations. Developed by researcher Ponkoj Chandra Shill, the system addresses a critical gap in current automated approaches that often assume clean text input or apply vision models without forensic justification. The framework explicitly determines the presence and source of textual evidence, distinguishing between three types: embedded text within images, 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 (ViT) backbones. This conditional inference approach mirrors actual forensic decision-making processes, improving evidentiary traceability while avoiding unjustified assumptions about modality availability. The experimental evaluation on forensic-style image evidence demonstrates consistent and interpretable behavior across heterogeneous evidence scenarios, representing a significant advancement over current methods that struggle with the messy, multimodal nature of real forensic evidence.

The research, published on arXiv with identifier 2604.08609, represents an 8-page technical contribution to the fields of computer vision, artificial intelligence, and machine learning. By creating a system that can handle the complex interplay between different types of evidence in forensic investigations, this work moves beyond traditional single-modality approaches to provide investigators with more reliable, interpretable tools for detecting harmful content in digital evidence.