Abstract: With the rapid development of Deepfake technology,forged facial images and videos generated by such techniques have become increasingly prevalent on social media platforms.However,these technologies are also being maliciously exploited,posing serious threats to social security.Although existing detection methods perform well in detecting Deepfake faces on in-domain datasets,their performance significantly degrades when applied to unseen datasets.To address this issue,a Deepfake detection method based on positional enhancement and frequency domain component interaction is proposed,aiming to improve the robustness and generalization of facial forgery detection.Firstly,vision Transformer is employed as the backbone network to capture forgery traces from a global perspective.Secondly,the dynamic local feature extraction module is designed,utilizing channel-wise and point-wise convolutional operations for local feature extraction.This module dynamically weights features based on pixel-level importance in feature representation,thereby refining local features and enhancing the ability to perceive local features.Concurrently,the multi-scale feature extraction and positional enhancement module is constructed,which acquires multi-scale features through multi-dilated convolutions and introduces a positional enhancement mechanism to strengthen positional correlations between pixels,effectively extracting multi-scale information from different regions.Then,the global-local frequency domain component interaction module is developed,implementing information exchange between different frequency components through the frequency domain decomposition attention mechanism.This captures dependencies between global and local features to identify artifacts that disappear in RGB space when fake facial image quality degrades.Finally,the pixel relationship similarity loss function is designed to calculate positional relationship losses between pixels and is combined with cross-entropy loss to construct the joint loss function to improve detection accuracy.Experimental results demonstrate that the proposed method achieves AUC scores of 99.29% and 78.62% on FF++ and Celeb-DF datasets respectively,proving its effectiveness in enhancing the robustness and generalization of facial forgery detection.

Key words: Feature extraction, Positional enhancement, Frequency domain component interaction, Joint loss, Deepfake detection