## 1 Introduction
(2018) proposed a new long-range attention mechanism to capture global semantic inconsistencies in forgery samples, which reduces the complexity of the model and achieves good detection results. Therefore, to address the above problems, this manuscript proposes an improved deep forgery face detection method mainly focusing on the existing deep-learning-based deep forgery detection methods with noise artifacts, insufficient feature extraction capability in the face of a deep forgery identification task, and poor detection performance in the face of sample confrontation attack. The framework of deepfake face detection and adversarial attack defense method based on multi-feature decision fusion is shown in Figure 1, which includes the use of improved IMTCNN to accurately extract face faces and a diffusion model for noise reduction and de-artificing of the forgery face data, the addition of the SENet attention mechanism to the FG-TEFusionNet network, WADAN, and an image texture enhancement module on the FG-TEFusionNet network to improve the deep neural network for the acquisition of deep information and texture features of the face in the video image; add samples in the network during the training process against the attack I-FGSM algorithm to generate samples against the attack; and then use Adversarial Training for defense, for the accuracy, robustness, and security of the experimental dataset for the research and analysis. Figure 1 illustrates the overall framework and workflow of the proposed method, with each component linked to the main contributions; to enhance the method's reproducibility and clarity, this paper adopted a four-stage processing workflow: (1) input preprocessing resizes raw video frames to 384 \(\times\) 384 pixels and normalizes them, locates facial features via the IMTCCN network, and achieves denoising and artifact removal using a diffusion model; (2) the spatiotemporal feature extraction stage processes geometric and texture feature streams in parallel, where the geometric branch calibrates key points with LRNet and calculates inter-frame attention through SENet, while the texture branch extracts features via EfficientNet and detects spectral anomalies through the collaboration of Gram matrices and WSDAN modules; (3) adversarial optimization generates adversarial samples based on I-FGSM to update model weights, yielding preliminary inter-frame/intra-frame detection results; (4) decision fusion output produces frame-level authenticity predictions. This structured workflow complements the visualization in Figure 1 to ensure algorithmic transparency. Figure 1: The framework of deepfake face detection and the adversarial attack defense method based on multi-feature decision fusion. (It encompasses four end-to-end workflows, including an input preprocessing module (denoising/artifact removal), inter-frame and intra-frame feature extraction, decision fusion, and adversarial attack and defense.). The main contributions of this manuscript are as follows:  
(1) The IMTCCN architecture was systematically improved to achieve high-precision facial feature extraction through the integration of a diffusion model, which effectively suppresses noise and artifacts. Qualitative evaluations and quantitative metrics jointly validate the enhanced operational efficacy of this optimized framework. (2) A novel multi-feature decision fusion model named FG-TEFusionNet is proposed for deepfake detection, which consists of two specialized modules: the SEAR-Net and the TE-WSDAN-DDNet. The SEAR-Net enhances inter-frame dependency modeling by integrating SENet attention mechanisms into a dual-stream RNN architecture based on the LRNet baseline, enabling preliminary predictions through frame-sequence correlation analysis. Simultaneously, the TE-WSDAN-DDNet embeds a Gram image texture module within the EfficientNet backbone, fusing feature maps from the Weakly Supervised Data Augmentation Network (WSDAN) to overcome geometric method limitations through deep texture pattern extraction.