Detection of image splicing forgery become an increasingly difficult task due to the scale variations of the forged areas and the covered traces of manipulation from post-processing techniques. Most existing methods fail to jointly multi-scale local and global information and ignore the correlations between the tampered and real regions in inter-image, which affects the detection performance of multi-scale tampered regions. To tackle these challenges, in this paper, we propose a novel method based on feature aggregation and region-aware learning to detect the manipulated areas with varying scales. In specific, we first integrate multi-level adjacency features using a feature selection mechanism to improve feature representation. Second, a cross-domain correlation aggregation module is devised to perform correlation enhancement of local features from CNN and global representations from Transformer, allowing for a complementary fusion of dual-domain information. Third, a region-aware learning mechanism is designed to improve feature discrimination by comparing the similarities and differences of the features between different regions. Extensive evaluations on benchmark datasets indicate the effectiveness in detecting multi-scale spliced tampered regions.

Recognizing and understanding various objects in visual information is paramount for ensuring safe and efficient autonomous navigation, especially in challenging environmental conditions. However, relying solely on single-modal data to perceive information, such as visible images, can compromise the quality and reliability of the extracted information, posing potential risks to autonomous driving systems. To address this challenge, we present a novel fusion method based on mutual information theory in semantic segmentation tasks for secure and efficient autonomous vehicles. The proposed method involves two primary components, i.e., multispectral fusion representation module (FRM) and semantic segmentation module (SSM). To optimize the FRM, we establish a unified quality representation for feature fusion by incorporating an image restoration mechanism, enhancing autonomous driving systems' overall performance and adaptability in complex environments. Meanwhile, we employ mutual information maximization to capture the interimage relations among pixels from the same semantic content, which is achieved by leveraging the semantic representation learned by the SSM in a high-dimensional feature space. Experimental results and comparisons with famous fusion approaches and segmentation models on four public datasets validate our method's effectiveness, robustness, and overall superiority.