Omnimodal Vigilance: Architecting a Unified Neural Framework to Disrupt Cross-Channel Child Exploitation
DOI:
https://doi.org/10.70917/ijcisim-2026-3106Keywords:
Online Child Exploitation, Multimodal Deep Learning, Cross-Channel Analysis, Federated Edge Intelligence, Autonomous Cyber Prevention, Digital Forensics, Vision Transformers, Behavioral Biometrics, Anomaly DetectionAbstract
The proliferation of interconnected digital ecosystems has catalyzed a paradigm shift in online child exploitation (OCE), transitioning predatory behavior from isolated interactions into sophisticated, cross-channel campaigns. Conventional digital forensic architectures fundamentally rely on unimodal detection systems, isolating text, image, or acoustic analysis into separate computational pipelines. Consequently, these models suffer from severe contextual blindness, failing to identify cohesive grooming strategies that pivot across platforms or utilize encrypted environments. To resolve these systemic vulnerabilities, this paper introduces the Unified Omnimodal Deep Fusion Architecture for Cross-Channel Exploitation Prevention (UODFA-CCEP). The proposed framework operates as a master intelligence layer, orchestrating ten highly specialized sub-frameworks across five modalities: text (SATE-PIR, RT-DSL), image (AR-ViT, DSA-CHE), audio (RT-MSFA, SA-W2V), video (ST-DSA, AVST-AD), and behavioral sensors (PP-FEA, RT-MSFE). The UODFA-CCEP is uniquely engineered into three interconnected components. The Detection Engine employs cross-modal attention mechanisms to fuse high-dimensional feature spaces, generating real-time exploitation risk scores. Simultaneously, the Prevention Engine eradicates the traditional human-in-the-loop reporting bottleneck by autonomously triggering emergency escalation protocols, including automated law enforcement notification, cyber crime department reporting, and GPS-guided WhatsApp incident routing. Evaluated against an augmented, real-world forensic corpus, the UODFA-CCEP unified ecosystem achieved an unprecedented 98.9% F1-score with a micro-averaged prevention dispatch latency of under 150 milliseconds. The empirical results demonstrate that unifying multidimensional detection with deterministic autonomous prevention establishes a highly scalable, privacy-preserving, and deployable standard for proactive child protection intelligence in the modern digital landscape.
