The rapid development of generation models has brought potential risks, necessitating generated image detection. In the meantime, evaluating the generalization to unseen diffusion models has become the detectors’ major challenge. Existing methods attempt to train detectors using the reconstruction features obtained from diffusion reconstruction, but they use these features in a limited way, resulting in a lack of generalization ability. Therefore, we propose that there are some extra forgery clues implicit in the input image and reconstruction features, and design a Dual Branch re-Extraction (DBE) module to extract them. To obtain a more generalized feature representation, spatial and frequency features are extracted through the calculation of neighboring pixel relationships and the DWT-based high-frequency feature extractor, respectively. Extensive experiments demonstrate the superior performance of our method, with an average improvement of 5%/7% in AUROC/AP compared to recent state-of-the-art works.

Causal reasoning algorithms can evaluate the strength of causal relationships between events and play an essential role in revealing and understanding the underlying mechanisms of them. However, traditional algorithms find it difficult to handle the complex interaction effects between numerous variables, can only consider event information at a single moment, and are challenging to solve the uncertainty problem in temporal evolution. To address these challenges, this paper proposes a temporal knowledge graph causal reasoning model (TKGR) that combines fuzzy logic and graph attention networks. Graph attention networks are used to capture the complex interaction effects between numerous variables at a single moment. At the same time, a multi-head self-attention mechanism is used to aggregate temporal information from multiple time points. In response to the uncertainty problem in time series evolution, we also designed a fuzzy logic module to comprehensively consider the importance of features from different time points. Finally, this paper carried out multiple rounds of comparative experiments with traditional causal reasoning algorithms and ablation experiments on the simulation dataset of satellite reconnaissance missions. The results show that the TKGR model can effectively reason the strength of causal relationships, thus providing necessary reference for decisionmaking.