With the development of deep learning technology, the facial images generated by deepfake technology have reached a level of authenticity that is difficult to distinguish, posing a serious threat to personal privacy and data security. Therefore, it is of great significance to develop efficient and reliable deepfake detection technology. In recent years, Visual Language Models (VLM) have been applied to deepfake detection tasks due to their powerful multimodal understanding capabilities. However, the existing VLM have not been specifically optimized for deepfake detection tasks. When directly applied to this task, there are problems such as insufficient model accuracy and insufficient feature extraction, especially when dealing with complex forgery scenes. In response to these challenges, this paper proposes an innovative deepfake face detection method based on VLM and component-specific prompt tuning. We transform the deepfake detection task into a Visual Question Answering (VQA) task, making full use of the multimodal understanding capabilities of VLM and the flexibility of prompt tuning technology. This method uses a local prompt strategy to customize specific prompt questions for key facial components such as eyes, nose, and mouth, guiding the model to focus on the local features of these areas, thereby accurately capturing forgery traces. In addition, we introduced a feature extraction module Q-Former based on instructions, which can flexibly adjust the focus area of visual features according to prompts, significantly improving the model’s perception of locally forged features. By fusing these local features extracted by Q-Former and combining them with the language model to judge the authenticity of the overall face image, we can finally generate accurate prediction results. A large number of experimental results show that our method is significantly better than existing technologies in terms of detection accuracy and robustness.

Myocardial pathology segmentation (MyoPS) is critical for the risk stratification and treatment planning of myocardial infarction (MI). Multi-sequence cardiac magnetic resonance (MS-CMR) images can provide valuable information. For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively. Existing methods usually fuse anatomical and pathological information from different CMR sequences for MyoPS, but assume that these images have been spatially aligned. However, MS-CMR images are usually unaligned due to the respiratory motions in clinical practices, which poses additional challenges for MyoPS. This work presents an automatic MyoPS framework for unaligned MS-CMR images. Specifically, we design a combined computing model for simultaneous image registration and information fusion, which aggregates multi-sequence features into a common space to extract anatomical structures (i.e., myocardium). Consequently, we can highlight the informative regions in the common space via the extracted myocardium to improve MyoPS performance, considering the spatial relationship between myocardial pathologies and myocardium. Experiments on a private MS-CMR dataset and a public dataset from the MYOPS2020 challenge show that our framework could achieve promising performance for fully automatic MyoPS.