A Deepfake Image Detection Method Based on a Multi-Graph Attention Network

Deep forgery detection plays a crucial role in addressing the challenges posed by the rapid spread of deeply generated content that significantly erodes public trust in online information and media. Deeply forged images typically present subtle but significant artifacts in multiple regions, such as in the background, lighting, and localized details. These artifacts manifest as unnatural visual distortions, inconsistent lighting, or irregularities in subtle features that break the natural coherence of the real image. To address these features of forged images, we propose a novel and efficient deep image forgery detection method that utilizes Multi-Graph Attention (MGA) techniques to extract global and local features and minimize accuracy loss. Specifically, our method introduces an interactive dual-channel encoder (DIRM), which aims to extract global and channel-specific features and facilitate complex interactions between these feature sets. In the decoding phase, one of the channels is processed as a block and combined with a Dynamic Graph Attention Network (PDGAN), which is capable of recognizing and amplifying forged traces in local information. To further enhance the model’s ability to capture global context, we propose a global Height–Width Graph Attention Module (HWGAN), which effectively extracts and associates global spatial features. Experimental results show that the classification accuracy of our method for forged images in the GenImage and CIFAKE datasets is comparable to that of the optimal benchmark method. Notably, our model achieves 97.89% accuracy on the CIFAKE dataset and has the lowest number of model parameters and lowest computational overhead. These results highlight the potential of our method for deep forgery image detection.