AUTHOR=Hussain Tarak , Reddy B. Tirapathi , Phanindra Kondaveti , Terumalasetti Sailaja , Khan Ghufran Ahmad 
  
TITLE=Decoding deception: state-of-the-art approaches to deep fake detection
  
JOURNAL=Frontiers in Big Data
  
VOLUME=Volume 8 - 2025
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1670833
  
DOI=10.3389/fdata.2025.1670833
  
ISSN=2624-909X
  
ABSTRACT=Deepfake technology evolves at an alarming pace, threatening information integrity and social trust. We present new multimodal deepfake detection framework exploiting cross-domain inconsistencies, utilizing audio-visual consistency. Its core is the Synchronization-Aware Feature Fusion (SAFF) architecture combined with Cross-Modal Graph Attention Networks (CM-GAN), both addressing the temporal misalignments explicitly for improved detection accuracy. Across eight models and five benchmark datasets with 93,750 test samples, the framework obtains 98.76% accuracy and significant robustness against multiple compression levels. Synchronized audio-visual inconsistencies are thus highly discriminative according to statistical analysis (Cohen's d = 1.87). With contributions centering around a cross-modal feature extraction pipeline, a graph-based attention mechanism for inter-modal reasoning and an extensive number of ablation studies validating the fusion strategy, the paper also provides statistically sound insights to guide future pursuit in this area. With a 17.85% generalization advantage over unimodal methods, the framework represents a new state of the art and introduces a self-supervised pre-training strategy that leverages labeled data 65% less.