版权保护和内容认证的全盲双功能数字水印算法

针对现有大部分水印算法功能单一且无法实现完全盲检测的局限性,提出一种同时进行版权保护和内容认证的全盲双功能数字水印算法.算法采取子块区域分割的思想,水印产生和嵌入分别利用同一个子块的不同区域.通过判别两个最大奇异值均值的最高位奇偶性产生特征水印,然后自适应地调整相邻区域相同位置的两个离散余弦变换交流系数的大小,实现水印的嵌入.检测端通过产生的特征水印和盲提取的认证水印实现全盲的版权鉴别和内容认证.相比于已报道的一些水印方案,本文提出的水印算法对于常见的攻击具有更好的顽健性,可以实现版权保护功能,同时对于恶意的图像篡改又有较好的敏感性,可以准确定位篡改的区域.     

采用前置栅网滤波的红外点目标探测方法

根据红外热成像系统前置栅网的衍射效应,分析其应用于点目标探测领域的可行性;提出了一种基于前置栅网滤波的红外点目标探测方法,详细介绍了算法处理流程,该算法通过背景抑制、图像分割、图形匹配有效地实现了对前置栅网滤波后的点目标像探测;最后,将其与传统的TBD探测方法进行比较。实验结果表明:该方法由于使点目标实现了特定形状的扩展,能更好地区分真实目标和噪声,同时降低算法复杂度,能够实现实时处理,具有很好的应用前景。     

Lithium Tritelluride as an Electrolyte Additive for Stabilizing Lithium Deposition and Enhancing Sulfur Utilization in Anode-Free Lithium–Sulfur Batteries

Despite the potential to become the next-generation energy storage technology, practical lithium–sulfur (Li–S) batteries are still plagued by the poor cyclability of the lithium-metal anode and sluggish conversion kinetics of S species. In this study, lithium tritelluride (LiTe₃), synthesized with a simple one-step process, is introduced as a novel electrolyte additive for Li–S batteries. LiTe₃ quickly reacts with lithium polysulfides and functions as a redox mediator to greatly improve the cathode kinetics and the utilization of active materials in the cathode. Moreover, the formation of a Li₂TeS₃/Li₂Te-enriched interphase layer on the anode surface enhances ionic transport and stabilizes Li deposition. By regulating the chemistry on both the anode and cathode sides, this additive enables a stable operation of anode-free Li–S batteries with only 0.1 m concentration in conventional ether-based electrolytes. The cell with the LiTe₃ additive retains 71% of the initial capacity after 100 cycles, while the control cell retains only 23%. More importantly, with high utilization of Te, the additive enables significantly better cyclability of anode-free pouch full-cells under lean electrolyte conditions.     

An All-Stretchable,Ultraviolet Protective,and Electromagnetic-Interference-Free E-Textile

Flexible and skin-mountable electronics have drawn tremendous research attention with the booming of smart medical systems and wearing technologies, however, their environmental adaptability to electromagnetic and solar radiation has long been neglected. Herein, a novel health monitoring e-textile with robust ultraviolet (UV) protecting and strong electromagnetic interference (EMI) shielding performance is rationally developed on an ultraelastic and bilayered nonwoven textile. Via the respective incorporation of silver flake-modified liquid metal (AgLM) and silver nanoparticles (AgNPs) on each side of a permeable substrate, a Janus sensing layer with electrophysiological monitoring function, Joule heating ability, and excellent EMI shielding capability (up to 38.5 dB in X band) is first fabricated. Elastic microfibers embedded with sensitive photochromic microcapsules are then in situ assembled on the bioelectric-sensing layer, achieving a bilayered e-textile with a reversible UV-chromic property and an extraordinary UV protection factor (UPF) of 335.56. The developed all-stretchable and UV-EMI proof e-textile is utilized as a safe and comfortable on-skin electronic to provide point-of-care health regulation under complex UV/EMI radiative environments. Specifically, stable Joule heating performance and accurate monitoring of electrocardiogram (ECG) and surface electromyography (sEMG) are simultaneously obtained, demonstrating promising applications in multifunctional and robust wearing electronics.     

Review on application progress of federated learning model and security hazard protection

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy. As data privacy becomes more important, it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data security. Data is forced to be stored independently between companies, creating “data silos”. With the goal of safeguarding data privacy and security, the federated learning framework greatly expands the amount of training data, effectively improving the shortcomings of traditional machine learning and deep learning, and bringing AI algorithms closer to our reality. In the context of the current international data security issues, federated learning is developing rapidly and has gradually moved from the theoretical to the applied level. The paper first introduces the federated learning framework, analyzes its advantages, reviews the results of federated learning applications in industries such as communication and healthcare, then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications, and finally looks into the future of federated learning and the application layer.     

Total Synthesis of (E/Z) des-Hydroxy Triticone A and B

The first total synthesis of des-hydroxy triticone A and B was achieved in 12 steps. The highly chemoselective, base mediated iodolactamization of olefins with N-OMe amide motif is a key highlight of the synthesis.     

A secure fingerprint template generation mechanism using visual secret sharing with inverse halftoning

Fingerprints are the most popular and widely practiced biometric trait for human recognition and authentication. Due to the wide approval, reliable fingerprint template generation and secure saving of the generated templates are highly vital. Since fingers are permanently connected to the human body, loss of fingerprint data is irreversible. Cancelable fingerprint templates are used to overcome this problem. This paper introduces a novel cancelable fingerprint template generation mechanism using Visual Secret Sharing (VSS), data embedding, inverse halftoning, and super-resolution. During the fingerprint template generation, VSS shares with some hidden information are formulated as the secure cancelable template. Before authentication, the secret fingerprint image is reconstructed back from the VSS shares. The experimental results show that the proposed cancelable templates are simple, secure, and fulfill all the properties of the ideal cancelable templates, such as security, accuracy, non-invertibility, diversity, and revocability. The experimental analysis shows that the reconstructed fingerprint images are similar to the original fingerprints in terms of visual parameters and matching error rates.     

Protecting the trust and credibility of data by tracking forgery trace based on GANs

With the advent of the 5G Internet of Things era, communication and social interaction in our daily life have changed a lot, and a large amount of social data is transmitted to the Internet. At the same time, with the rapid development of deep forgery technology, a new generation of social data trust crisis has also followed. Therefore, how to ensure the trust and credibility of social data in the 5G Internet of Things era is an urgent problem to be solved. This paper proposes a new method for forgery detection based on GANs. We first discover the hidden gradient information in the grayscale image of the forged image and use this gradient information to guide the generation of forged traces. In the classifier, we replace the traditional binary loss with the focal loss that can focus on difficult-to-classify samples, which can achieve accurate classification when the real and fake samples are unbalanced. Experimental results show that the proposed method can achieve high accuracy on the DeeperForensics dataset and with the highest accuracy is 98%.     

PRA-TPE: Perfectly Recoverable Approximate Thumbnail-Preserving Image Encryption

With the popularity of cloud servers, an increasing number of people are willing to store their images in the cloud due to many conveniences such as online browsing and managing images. On the other hand, this inevitably causes users’ concerns about image privacy leakage. Many image encryption schemes are proposed to prevent privacy leakage, while most of them focus only on privacy protection and ignore the usability of encrypted images. For this purpose, Marohn et al. (2017) designed two approximate thumbnail-preserving encryption (TPE) schemes to balance image privacy and usability. However, the decrypted image in these two schemes are only perceptually close to the original one and the original image cannot be perfectly recovered. To this end, we design a perfectly recoverable approximate TPE scheme in this paper, which combines reversibledata hiding (RDH) with encryption schemes. The thumbnails of the original and processed images are similar to balance image privacy and usability well. Meanwhile, the reversibility of RDH and encryption schemes is utilized to ensure the perfect recoverability in the proposed scheme. Experiments show that the proposed approximate TPE scheme is no longer limited to balancing usability and privacy but attains perfect recovery.