# A Spatial-Frequency Aware Multi-Scale Fusion Network for Real-Time Deepfake Detection

## TL;DR

This paper introduces SFMFNet, a lightweight multi-scale neural network that effectively detects deepfakes in real-time by combining spatial textures and frequency artifacts with efficient feature interaction mechanisms.

## Contribution

The paper presents a novel spatial-frequency hybrid module and a token-selective cross attention mechanism to improve real-time deepfake detection efficiency and accuracy.

## Key findings

- Achieves high accuracy with low computational cost
- Demonstrates strong generalization across datasets
- Balances detection performance and real-time efficiency

## Abstract

With the rapid advancement of real-time deepfake generation techniques, forged content is becoming increasingly realistic and widespread across applications like video conferencing and social media. Although state-of-the-art detectors achieve high accuracy on standard benchmarks, their heavy computational cost hinders real-time deployment in practical applications. To address this, we propose the Spatial-Frequency Aware Multi-Scale Fusion Network (SFMFNet), a lightweight yet effective architecture for real-time deepfake detection. We design a spatial-frequency hybrid aware module that jointly leverages spatial textures and frequency artifacts through a gated mechanism, enhancing sensitivity to subtle manipulations. A token-selective cross attention mechanism enables efficient multi-level feature interaction, while a residual-enhanced blur pooling structure helps retain key semantic cues during downsampling. Experiments on several benchmark datasets show that SFMFNet achieves a favorable balance between accuracy and efficiency, with strong generalization and practical value for real-time applications.