The rapid development of AIGC technology has enabled highly realistic forged images to deceive human perception, posing serious risks across many areas. Current deepfake image detection methods primarily identify forgeries by extracting handcrafted features, deep features, and frequency-domain features. While these features contain forgery traces, they also include a substantial amount of the image's semantic information, which interferes with the precision and generalization of forgery detection models. To tackle these challenges, this paper introduces a novel forgery image identification method based on the Spatial-Temporal Forgery Trace (STFT). Motivated by the fact that forgery images are more easily fitted to a specific distribution than real images, the STFT method approaches the issue from a forged image distribution modeling perspective, employing generative diffusion models to meticulously capture the temporal distribution of images. It further models the relationship between temporal feature variations and spatially corresponding temporal features, treating them as temporal and spatial forgery traces. Moreover, STFT incorporates frequency-domain features as weighting factors to accelerate the localization of spatio-temporal forgery traces. Experiments demonstrate that by integrating spatial, temporal, and frequency perspectives within the latent space, STFT effectively captures subtle spatio-temporal forgery traces, exhibiting strong robustness and generalizability. It outperforms state-of-the-art methods on major benchmark datasets in the field. The source code for STFT is available at \href{https://anonymous.4open.science/r/STFT-B552/}.