In copy-move forgery, the illumination and contrast of tampered and genuine regions are highly consistent, which poses a greater challenge in Copy-Move Forgery Detection (CMFD). In this paper, an end-to-end neural network is proposed based on Adaptive attention and Residual refinement Network (AR-Net). Specifically, position and channel attention features are fused by the adaptive attention mechanism to fully capture context information and enrich the representation of features. Secondly, deep matching is adopted to compute the self-correlation between feature maps, and Atrous Spatial Pyramid Pooling (ASPP) fuses the scaled correlation maps to generate the coarse mask. Finally, the coarse mask is optimized through the residual refinement module, which retains the structure of objects boundary. Extensive experiments, evaluated on CASIAII, COVERAGE and CoMoFoD datasets, demonstrate that the AR-Net has superior performance than state-of-the-art algorithms and can locate tampered and corresponding genuine regions in pixel level. In addition, AR-Net has high robustness on post-processing operations, such as noise, blur, JPEG recompression.

Change Detection (CD) is an important research topic in the remote sensing field, and it has a wide range of applications, including resource monitoring, disaster assessment, urban planning, etc. Recently, Deep Learning (DL) has shown its advantages in CD. However, most existing DL-based methods can not capture the complementary information between bi-temporal and difference features. This paper proposes an Edge-Guided Parallel Network (EGPNet) to solve this problem. First, our EGPNet extracts bi-temporal and difference features simultaneously through a parallel encoding framework. During parallel encoding, we design an Supplementary Mechanism (SM) to enrich the difference features with bi-temporal features. Second, we fuse bi-temporal and difference features at each feature level to sufficiently exploit their complementarity. Finally, the Edge-Aware Module (EAM) and Edge-Guidance Feature Module (EFM) are introduced to enhance the edge representation for improving blurred edges of detection results. Benefiting from the rich change-related information in difference features and detailed information in bi-temporal features, our EGPNet can detect change regions entirely and accurately. Experimental results on the LEVIR-CD, SYSU-CD and CDD datasets demonstrate that the proposed method outperforms several state-of-the-art approaches. Especially our EGPNet can detect more precise and sharper edges than other methods. We open source the Pytorch implementation and the pre-trained model at https://github.com/Lvkyky/EGPNet

Few-shot remote sensing scene classification (FSRSSC) aims to identify unseen scene classes from limited labeled samples, facing the challenge of accurately modeling data distribution and preserving image details in complex backgrounds with high intraclass variance and interclass similarity. To address this challenge, we propose a novel Diffusion Prototype Rectified Network (DiffPR-Net), which is comprised of three core modules: diffusion augmentation (DA), dual attention fusion module (DAFM) and prototype rectified module (PRM). The DA is constructed to generate high-quality remote sensing images with the objective of augmenting the training dataset. Besides, the DAFM facilitates the model to focus discriminative regions by transmitting highly fused image detail features from higher to lower layers. What’s more, the PRM addresses prototype deviation by adaptively assigning temporary labels to unlabeled data based on prediction confidence, thereby correcting the initial prototypes. Experiments indicate that our proposed method is highly promising, achieving competitive or state-of-the-art classification performance while addressing the scarcity of remotely sensed data and enhancing focus on discriminative regions.