Frequency Assisted Multiscale Dual-Stream Network for Low-Quality Deepfake Detection

With the rapid development of Deepfake technologies, the prevalence of forged facial images and videos on social media has surged. However, these technologies have also been exploited for malicious purposes, posing significant threats to social security. Although existing detection methods have shown effectiveness in identifying high-quality facial forgeries, their performance drops significantly for low-quality forgeries, as compression causes detail loss and feature blurring. To address this problem, a Frequency Assisted Multiscale Dual-Stream Network (FAMDnet) is proposed for low-quality Deepfake detection in this article. First, the multiscale spatial feature extraction module is designed to extract RGB features from image patches of different sizes, thereby capturing forgery traces within multiscale regions. Then, the dynamic global frequency feature extraction module is constructed by combining learnable dynamic filters with the Fast Fourier Transform to extract frequency domain features, supplementing the forgery artifacts that may vanish in the spatial domain. Finally, the multimodal attention fusion module is utilized to explore the correlation between RGB features and frequency domain features, effectively capturing local texture details and global context patterns in facial images, thereby achieving better fusion of RGB features and frequency domain features. Moreover, a parameter-free attention mechanism is introduced into the classifier to enhance the fused features, further improving the model's discriminative ability for facial forgery. Comparative experiments on the FaceForensics++, Celeb-DF, and WildDeepfake datasets demonstrate that FAMDnet has better performance in detecting low-quality fake images and videos. The code is available at https://github.com/daisy-12138/FAMDnet .