CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer

In this paper, we aim to devise a universally versatile style transfer method capable of performing artistic, photo-realistic, and video style transfer jointly, without seeing videos during training. Previous single-frame methods assume a strong constraint on the whole image to maintain temporal consistency, which could be violated in many cases. Instead, we make a mild and reasonable assumption that global inconsistency is dominated by local inconsistencies and devise a generic Contrastive Coherence Preserving Loss (CCPL) applied to local patches. CCPL can preserve the coherence of the content source during style transfer without degrading stylization. Moreover, it owns a neighbor-regulating mechanism, resulting in a vast reduction of local distortions and considerable visual quality improvement. Aside from its superior performance on versatile style transfer, it can be easily extended to other tasks, such as image-to-image translation. Besides, to better fuse content and style features, we propose Simple Covariance Transformation (SCT) to effectively align second-order statistics of the content feature with the style feature. Experiments demonstrate the effectiveness of the resulting model for versatile style transfer, when armed with CCPL.Comment: Accepted by ECCV2022 as an oral paper; code url: https://github.com/JarrentWu1031/CCPL Video demo: https://youtu.be/scZuJCXhL1

Enhancing Scene Text Detectors with Realistic Text Image Synthesis Using Diffusion Models

Scene text detection techniques have garnered significant attention due to their wide-ranging applications. However, existing methods have a high demand for training data, and obtaining accurate human annotations is labor-intensive and time-consuming. As a solution, researchers have widely adopted synthetic text images as a complementary resource to real text images during pre-training. Yet there is still room for synthetic datasets to enhance the performance of scene text detectors. We contend that one main limitation of existing generation methods is the insufficient integration of foreground text with the background. To alleviate this problem, we present the Diffusion Model based Text Generator (DiffText), a pipeline that utilizes the diffusion model to seamlessly blend foreground text regions with the background's intrinsic features. Additionally, we propose two strategies to generate visually coherent text with fewer spelling errors. With fewer text instances, our produced text images consistently surpass other synthetic data in aiding text detectors. Extensive experiments on detecting horizontal, rotated, curved, and line-level texts demonstrate the effectiveness of DiffText in producing realistic text images

SSL/TLS Certificates and Their Prevalence on the Dark Web (First Report)

As organizations focus on the digital transformation of their businesses, the importance of encryption as the cornerstone of security and privacy is increasingly vital. In 2018, over 70 percent of internet traffic was encrypted. Experts believe that this figure is expected to rise to 80 percent in 2019 (Google, 2019). Secure Sockets Layer (SSL, an older standard) and Transport Layer Security (TLS, a newer standard) certificates are essential to encryption because they authorize all encrypted communication between machines. SSL/TLS certificates are instrumental in protecting privacy and improving security, providing each machine with a unique machine identity. They control the flow of sensitive data to authorized machines and are used in everything from website transactions and mobile devices to smart city initiatives, robots, artificial intelligence algorithms and containers in the cloud. Despite the pivotal role encryption plays in our digital economy and across the internet, the processes needed to protect digital certificates are not well understood or widely followed. As a result, SSL/TLS certificates are often poorly protected, making them attractive targets for attackers. In fact, illegitimate access to SSL/TLS certificates has played a key role in several high-profile, high-impact breaches—such as Snowden, Sony and Equifax. To shine a light on the availability of SSL/TLS certificates on the dark web, the Evidence-based Cybersecurity Research Group at the Andrew Young School of Policy Studies at Georgia State University and the University of Surrey spearheaded a research program, sponsored by Venafi. This report details the preliminary findings of the research and outlines the volume of SSL/TLS certificates for sale on the dark web, including information on how they are packaged and sold to attackers. These certificates can be used to eavesdrop on sensitive communications, spoof websites, trick consumers and steal data. The long-term goal of this research is to gain a more thorough understanding of the role SSL/TLS certificates play in the economy of the dark web as well as how they are being used by attackers. This is the first of three reports—the first of their kind— focused on the underground SSL/TLS marketplace and its role in the wider cybercrime economy. This report will show that there is a machine identity-as-a-service marketplace on the dark web, where fraudulent TLS certificates are readily available for purchase

A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability

In the past few years, there has been an explosive surge in the use of machine learning (ML) techniques to address combinatorial optimization (CO) problems, especially mixed-integer linear programs (MILPs). Despite the achievements, the limited availability of real-world instances often leads to sub-optimal decisions and biased solver assessments, which motivates a suite of synthetic MILP instance generation techniques. However, existing methods either rely heavily on expert-designed formulations or struggle to capture the rich features of real-world instances. To tackle this problem, we propose G2MILP, the first deep generative framework for MILP instances. Specifically, G2MILP represents MILP instances as bipartite graphs, and applies a masked variational autoencoder to iteratively corrupt and replace parts of the original graphs to generate new ones. The appealing feature of G2MILP is that it can learn to generate novel and realistic MILP instances without prior expert-designed formulations, while preserving the structures and computational hardness of real-world datasets, simultaneously. Thus the generated instances can facilitate downstream tasks for enhancing MILP solvers under limited data availability. We design a suite of benchmarks to evaluate the quality of the generated MILP instances. Experiments demonstrate that our method can produce instances that closely resemble real-world datasets in terms of both structures and computational hardness. The deliverables are released at https://miralab-ustc.github.io/L2O-G2MILP

Explicit Interaction for Fusion-Based Place Recognition

Fusion-based place recognition is an emerging technique jointly utilizing multi-modal perception data, to recognize previously visited places in GPS-denied scenarios for robots and autonomous vehicles. Recent fusion-based place recognition methods combine multi-modal features in implicit manners. While achieving remarkable results, they do not explicitly consider what the individual modality affords in the fusion system. Therefore, the benefit of multi-modal feature fusion may not be fully explored. In this paper, we propose a novel fusion-based network, dubbed EINet, to achieve explicit interaction of the two modalities. EINet uses LiDAR ranges to supervise more robust vision features for long time spans, and simultaneously uses camera RGB data to improve the discrimination of LiDAR point clouds. In addition, we develop a new benchmark for the place recognition task based on the nuScenes dataset. To establish this benchmark for future research with comprehensive comparisons, we introduce both supervised and self-supervised training schemes alongside evaluation protocols. We conduct extensive experiments on the proposed benchmark, and the experimental results show that our EINet exhibits better recognition performance as well as solid generalization ability compared to the state-of-the-art fusion-based place recognition approaches. Our open-source code and benchmark are released at: https://github.com/BIT-XJY/EINet