In accomplishing the task of semantic segmentation of RGB-T remote sensing images, there is a great challenge due to severe occlusion, long-tailed data distribution, and insufficient pixel representation of certain objects. The effective use of high-level semantic contexts among various ground object categories is crucial, yet presents considerable diffi-culties. Traditional RGB-T remote sensing image semantic segmentation methods often fail to capture and utilize complex relationships and interdependencies among these categories, leading to reduced semantic segmentation accuracy. This paper proposes a novel, knowledge-enhancement module, empowering the model to utilize human-like commonsense knowledge. Specifically, we firstly employ the self-attention and cross-attention mechanisms to fuse RGB and Thermal features. Subsequently, we collect the weights of the classification layer to get a high-level semantic pool based on all the categories. Alongside this, a prior knowledge graph is developed to enable information propagation among all categories. We applied this knowledge-enhancement module to enhance the Mask R-CNN, named KEMask R-CNN. Experiments results on the RS RGB-T dataset demonstrate the progressiveness of the proposed method.

Deep learning-based video inpainting can fill the missing or undesired regions with spatial-temporal consistent contents without obvious visually distortion. Although the original purpose of deep inpainting is to repair flawed videos, it can also be adopted for malicious purposes, e.g., removal of specific objects. Therefore, automatically locating the inpainted regions is a challenging task in video forensics. This paper proposes a new forensic refinement framework to localize the deep inpainted regions by considering the spatial-temporal viewpoint. Firstly, we design a spatiotemporal convolution to suppress redundancy for highlighting deep inpainting traces. Then, a detection module is constructed with four concatenated ResNet blocks, and two upsampling layers to achieve a rough location map. Finally, a modified U-net based refinement module is developed for the pixel-wise localization map. Deep inpaiting video datasets created by the state-of-the-art deep inpainting method, have been evaluated, and extensive experimental results clearly demonstrate the efficacy of the proposed approach.

The reliability and security of multimedia contents in transmission, communications, storage, and usage have attracted special attention. Robust image hashing, also referred to as perceptual image hashing, is widely applied in multimedia authentication and forensics, image retrieval, image indexing, and digital image watermarking. In this work, a novel robust image hashing method based on quaternion Zernike moments (QZMs) is proposed. QZMs offer a sound way to jointly deal with the three channels of color images without discarding chrominance information; the generated hash is thus shorter than the hash of three channels separately processing. The proposed approach's performance was evaluated on the color images database of UCID and compared with several recent and efficient methods. These experiments show that the proposed scheme provides a short hash in length that is robust to most common image content-preserving manipulations like JPEG compression, filtering, noise, scaling, and large angle rotation operations.