Cross multiscale vision transformer for deep fake detection
This addresses the problem of digital media authenticity for security applications, but appears incremental as it applies existing methods to a new dataset.
The researchers tackled deep fake detection by evaluating various deep learning models on the SP Cup 2025 dataset, achieving unspecified performance results measured by accuracy metrics.
The proliferation of deep fake technology poses significant challenges to digital media authenticity, necessitating robust detection mechanisms. This project evaluates deep fake detection using the SP Cup's 2025 deep fake detection challenge dataset. We focused on exploring various deep learning models for detecting deep fake content, utilizing traditional deep learning techniques alongside newer architectures. Our approach involved training a series of models and rigorously assessing their performance using metrics such as accuracy.