Abstract: Reward-based crowdfunding platform fraud has now become a multimodal and temporally dynamic threat, with conventional text-only or snapshot-based detection methods ineffective at detecting more complex deceptive campaigns. In this study, a Temporal Dynamics Aware Multi-Model Fraud Detection Framework (TDMM-FDF) that simultaneously models linguistic indicators, visual discrepancies, and time behavioral changes is proposed. The framework introduces three key innovations: 1) HM4, a Hidden Method-of-Moments Markov model for modeling long-range latent transitions across campaign updates, 2) Polynomial Expansion Canonical Correlation Analysis (PECCA) for quantifying nonlinear semantic discrepancies between textual narratives and associated images, and 3) Frequency-Gated GRU (FG-GRU) which separates recurrent activations into low frequency (trend) and high frequency (anomaly) components in order to achieve higher sensitivity to abrupt fraudulent behaviors. Massive simulations on an actual Kickstarter data set prove that the given architecture outperforms classical machine learning models, sequence encoders, and transformer baselines significantly [96.4% accuracy and good calibration (ECE = 0.06) and high ROC-AUC]. The supplementary role of all modules is confirmed in ablation studies, and their qualitative analyses provide precise semantic-visual discrepancies and semantic time anomalies of fraudulent campaigns.

Lakshmi B S and Rekha K S. “Temporal-Cross-Modal Intelligence for Detecting Fraudulent Crowdfunding Campaigns”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161233

@article{S2025,
title = {Temporal-Cross-Modal Intelligence for Detecting Fraudulent Crowdfunding Campaigns},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161233},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161233},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Lakshmi B S and Rekha K S}
}