Traditional point-based image editing methods rely on iterative latent optimization or geometric transformations, which are either inefficient in their processing or fail to capture the semantic relationships within the image. These methods often overlook the powerful yet underutilized image editing capabilities inherent in pre-trained diffusion models. In this work, we propose a novel one-step point-based image editing method, named \textbf{AttentionDrag}, which leverages the inherent latent knowledge and feature correlations within pre-trained diffusion models for image editing tasks. This framework enables semantic consistency and high-quality manipulation without the need for extensive re-optimization or retraining. Specifically, we reutilize the latent correlations knowledge learned by the self-attention mechanism in the U-Net module during the DDIM inversion process to automatically identify and adjust relevant image regions, ensuring semantic validity and consistency. Additionally, AttentionDrag adaptively generates masks to guide the editing process, enabling precise and context-aware modifications with friendly interaction. Our results demonstrate a performance that surpasses most state-of-the-art methods with significantly faster speeds, showing a more efficient and semantically coherent solution for point-based image editing tasks. Code is released at: https://github.com/GPlaying/AttentionDrag.

High Click-Through Rate (CTR) imagery has proven commercial value for food delivery platforms, driving a need for strategies to generate visually compelling images. Our investigations reveal a positive correlation between appropriate food backgrounds and subsequent user engagement. Despite advancements in diffusion models, inpainting new backgrounds does not guarantee high CTR, and fine-tuning diffusion models for this purpose is prohibitively expensive for the fast-paced online food delivery advertising sector. Consequently, there is a lack of cost-effective, transferable generation frameworks tailored to high-CTR food images. In this paper, we propose FoRAGe, a novel high-CTR Food image Retrieval-Augmented Generation pipeline leveraging ControlNet based on Stable Diffusion. Specifically, we construct a comprehensive food image database encompassing a diverse range of background environments. During image generation, FoRAGe retrieves high-quality background exemplars featuring analogous food subjects from the database and employs the retrieved backgrounds as conditions to guide image synthesis via the ControlNet model. Subsequently, a multimodal CTR prediction model is utilized to identify and select optimal images for deployment. Extensive online experiments demonstrate a significant increase in CTR for images generated by our proposed pipeline, and ablation studies further elucidate the impact of different strategies and configurations. Code is available at https://github.com/jiaxu-feng/FoRAGe.

Owing to the ability of extracting features of images on long-range dependencies naturally, transformer is possible to reconstruct the damaged areas of images with the information from the uncorrupted regions globally. In this paper, we propose a two-stage framework based on a novel atrous pyramid transformer (APT) for image inpainting that recovers the structure and texture of an image progressively. Specifically, the patches of APT blocks are embedded in an atrous pyramid manner to explicitly enhance the correlation for both inter-and intra-windows to restore the high-level semantic structures of images more precisely, which could be served as a guide map for the second phase. Subsequently, a dual spectral transform convolution (DSTC) module is further designed to work together with APT to infer the low-level features of the generated areas. The DSTC module decouples the image signal into high frequency and low frequency for capturing texture information with a global view. Experiments on the CelebA-HQ, Paris StreetView, and Places2 demonstrate the superiority of the proposed approach.

Network function virtualization (NFV) has been promising to improve the availability, programmability, and flexibility of network function deployment and communication facilities. Meanwhile, with the advancements of cloud technologies, there has been a trend to outsource network functions through virtualization to a cloud service provider, so as to alleviate the local burdens on provisioning and managing such hardware resources. Promising as it is, redirecting the communication traffic to a third-party service provider has drawn various security and privacy concerns. Traditional end-to-end encryption can protect the traffic in transmit, but it also hinders data usability. This dilemma has raised wide interests from both industry and academia, and great efforts have been made to realize privacy-preserving network function outsourcing that can guarantee the confidentiality of network communications while preserving the ability to inspect the traffic. In this article, we conduct a comprehensive survey of the state-of-the-art literature on network function outsourcing, with a special focus on privacy and security issues. We first give a brief introduction to NFV and pinpoint its challenges and security risks in the cloud context. Then, we present detailed descriptions and comparisons of existing secure network function outsourcing schemes in terms of functionality, efficiency, and security. Finally, we conclude by discussing possible future research directions.