ArticleFrontiers in big data2025
Decoding deception: state-of-the-art approaches to deep fake detection.
Article in Frontiers in big data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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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
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