With the rise of AIGC technologies, particularly diffusion models, highly realistic fake images that can deceive human visual perception has become feasible. Consequently, various forgery detection methods have emerged. However, existing methods treat the generation process of fake images as either a black-box or an auxiliary tool, offering limited insights into its underlying mechanisms. In this paper, we propose Spatio-Temporal Distribution Fitting Deviation (STD-FD) for AIGC forgery detection, which explores the generative process in detail. By decomposing and reconstructing data within generative diffusion models, initial experiments reveal temporal distribution fitting deviations during the image reconstruction process. These deviations are captured through reconstruction noise maps for each spatial semantic unit, derived via a super-resolution algorithm. Critical discriminative patterns, termed DFactors, are identified through statistical modeling of these deviations. Extensive experiments show that STD-FD effectively captures distribution patterns in AIGC-generated data, demonstrating strong robustness and generalizability while outperforming state-of-the-art (SOTA) methods on major datasets. The source code is available at [this link](https://github.com/HengruiLou/STDFD).

In recent times, audio-driven lip-synching generation for digital humans has attracted considerable attention. However, the prevailing methodologies frequently encounter challenges pertaining to elevated computational complexity and deficient real-time performance. Although the MuseTalk framework has achieved notable progress in inference efficiency through its end-to-end, latent-space-based single-step generation algorithm, it still suffers from noticeable lip jitter and insufficient synchronization between audio and lip movements. To address these limitations, we propose an enhanced multi-frame inpainting framework that integrates Variational Autoencoders (VAE) and a multi-scale U-Net architecture. Specifically, our approach directly synthesizes the occluded lip region by leveraging multi-frame visual references combined with corresponding audio embeddings, thereby effectively improving lip synchronization and maintaining identity consistency. Furthermore, we introduce a landmark-guided multi-frame sampling strategy designed to enhance model attention towards lip dynamics. To facilitate deeper feature extraction and fusion, we propose a hierarchical latent-space feature fusion network (FusionNet), incorporating global and local residual connections and an enhanced Convolutional Block Attention Module. Additionally, a frame interpolation technique is employed during inference to further smooth lip movements and significantly mitigate lip jitter. The model has been trained on a large-scale Chinese dataset and comprehensively evaluated using both Chinese and English datasets. The experimental results demonstrate that the proposed framework achieves high visual accuracy, consistent lip synchronization, and efficient real-time inference, highlighting its strong cross-lingual generalization capability.