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\"The story of an infamous crime, a revered map dealer with an unsavory secret, and the ruthless subculture that consumed him Maps have long exerted a special fascination on viewers-both as beautiful works of art and as practical tools to navigate the world. But to those who collect them, the map trade can be a cutthroat business, inhabited by quirky and sometimes disreputable characters in search of a finite number of extremely rare objects. Once considered a respectable antiquarian map dealer, E. Forbes Smiley spent years doubling as a map thief -until he was finally arrested slipping maps out of books in the Yale University library. The Map Thief delves into the untold history of this fascinating high-stakes criminal and the inside story of the industry that consumed him. Acclaimed reporter Michael Blanding has interviewed all the key players in this stranger-than-fiction story, and shares the fascinating histories of maps that charted the New World, and how they went from being practical instruments to quirky heirlooms to highly coveted objects. Though pieces of the map theft story have been written before, Blanding is the first reporter to explore the story in full-and had the rare privilege of having access to Smiley himself after he'd gone silent in the wake of his crimes. Moreover, although Smiley swears he has admitted to all of the maps he stole, libraries claim he stole hundreds more-and offer intriguing clues to prove it. Now, through a series of exclusive interviews with Smiley and other key individuals, Blanding teases out an astonishing tale of destruction and redemption. The Map Thief interweaves Smiley's escapades with the stories of the explorers and mapmakers he knew better than anyone. Tracking a series of thefts as brazen as the art heists in Provenance and a subculture as obsessive as the oenophiles in The Billionaire's Vinegar, Blanding has pieced together an unforgettable story of high-stakes crime.\"-- Provided by publisher.

At present, security is significant for individuals and organizations. All information need security to prevent theft, leakage, alteration. Security must be guaranteed by applying some or combining cryptography algorithms to the information. Encipherment is the method that changes plaintext to a secure form called cipherment. Encipherment includes diverse types, such as symmetric and asymmetric encipherment. This study proposes an improved version of the advanced encryption standard (AES) algorithm called optimized advanced encryption standard (OAES). The OAES algorithm utilizes sine map and random number to generate a new key to enhance the complexity of the generated key. Thereafter, multiplication operation was performed on the original text, thereby creating a random matrix (4×4) before the five stages of the coding cycles. A random substitution-box (S-Box) was utilized instead of a fixed S-Box. Finally, we utilized the eXclusive OR (XOR) operation with digit 255, also with the key that was generated last. This research compared the features of the AES and OAES algorithms, particularly the extent of complexity, key size, and number of rounds. The OAES algorithm can enhance complexity of encryption and decryption by using random values, random S-Box, and chaotic maps, thereby resulting in difficulty guessing the original text.

With the emergence of the Internet of Things (IoT) and many smart gadgets that support artificial intelligence, it is easier than ever to acquire, reproduce, and disseminate a large number of digital data. However, these great technologies have made it possible for intruders to easily violate issues related to copyright protection, identity theft, and privacy leakage. To address such issues, several approaches have been developed, among which image watermarking has been proven to be an ideal solution. In this paper, a blind watermarking approach for RGB color images based on joint Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is proposed. First, a self-adaptive color selecting strategy is used to select either the blue or green channel of the host image for embedding purpose. Subsequently, the selected color is subdivided into non-overlapping square blocks of size 4 × 4, and then DCT is applied to each block. Afterward, the DC values obtained from each block are decomposed into four sub-bands using DWT, and then the LH middle frequency sub-band is further decomposed into four sub-bands using DWT. Lastly, the LH1 obtained from LH is utilized for watermark embedding. To provide security to the proposed approach, the watermark image is encrypted before embedding using a chaotic sequence originated from a logistic map method. Experimental results reveal that the proposed approach not only enhances watermark invisibility but also provide excellent watermark robustness, meeting the main requirements of image watermarking.

The escalating reliance on biometric systems for identity verification underscores the imperative for robust data protection mechanisms. Biometric authentication, leveraging unique biological and behavioral characteristics, offers unparalleled precision in individual identification. However, the integrity and confidentiality of biometric data remain paramount concerns, given its susceptibility to compromise. This research delineates the development and implementation of an innovative framework for cancellable biometrics, focusing on facial and fingerprint recognition. This study introduces a novel cancellable biometrics framework that integrates graph theory encryption with three-dimensional chaotic logistic mapping. The methodology encompasses a multifaceted approach: initially employing graph theory for the secure and efficient encryption of biometric data, subsequently enhanced by the complexity and unpredictability of three-dimensional chaotic logistic mapping. This dual-layered strategy ensures the robustness of the encryption, thereby significantly elevating the security of biometric data against unauthorized access and potential compromise. Thus, the resulting cancellable biometrics, characterized by the ability to transform biometric data into an adjustable representation, addresses critical challenges in biometric security. It allows for the revocation and reissuance of biometric credentials, thereby safeguarding the original biometric characteristics of individuals. This feature not only enhances user privacy and data security but also introduces a dynamic aspect to biometric authentication, facilitating adaptability across diverse systems and applications. Preliminary evaluations of the proposed framework demonstrate a marked improvement in the security of face and fingerprint recognition systems. Through the application of graph theory encryption, coupled with three-dimensional chaotic logistic mapping, our framework mitigates the risks associated with traditional biometric systems. This includes enhanced protection against data breaches, template theft, and the cloning of biometric identifiers. Therefore, the integration of graph theory and chaotic logistic mapping in cancellable biometrics presents a significant advancement in the field of biometric security. The proposed framework not only fortifies the encryption of biometric data but also introduces flexibility and resilience in the management of biometric templates. Future research will aim to refine this framework, exploring its applicability and effectiveness across a broader spectrum of biometric modalities and examining its potential for real-world deployment. Additionally, further studies will investigate the optimization of encryption algorithms and the scalability of the system to accommodate the growing demands of biometric authentication.

Despite the ease of digital image distribution, storage, and replication, averting identity theft, privacy breaches, and ownership issues can be challenging. Medical image encryption plays a vital role in ensuring the confidentiality of sensitive medical data and safeguarding patient privacy. This research addresses these concerns by introducing a novel approach, RISE , to medical image security by using the fusion of chaotic keys and a secret-sharing technique. The key advancement is the use of a Rubik’s cube-based bit-plane shuffling technique to reduce the complexity of strong image encryption, adding a unique dimension to the field of medical image security. Another distinguishing aspect of our approach is the strategic use of segmentation to encrypt only the sensitive part of the image and reduce the time complexity. This area is encrypted using a chaotic key with a Rubik’s cube-based bit-plane shuffling algorithm, followed by the implementation of the confusion process. The encrypted image is shared using a K-N Secret sharing method, which provides authentication and high robustness. The final decrypted image is enhanced using super-resolution to provide better information outputs. The proposed technique offers excellent security and produces better outcomes while being simple. The average NPCR and UACI scores of the proposed encryption technique are 99.47, and 49.90, respectively, and the entropy is 7.995, underscoring the robustness and effectiveness of our proposed approach. It has a high key bit sensitivity and average time complexity. The result analysis further ensures resistance against crop attacks or data loss, positioning it as a formidable contender in the landscape of modern image security.

Phosphorylation is a major regulator of protein interactions; however, the mechanisms by which regulation occurs are not well understood. Here we identify a salt-bridge competition or “theft” mechanism that enables a phospho-triggered swap of protein partners by Raf Kinase Inhibitory Protein (RKIP). RKIP transitions from inhibiting Raf-1 to inhibiting G-protein–coupled receptor kinase 2 upon phosphorylation, thereby bridging MAP kinase and G-Protein–Coupled Receptor signaling. NMR and crystallography indicate that a phosphoserine, but not a phosphomimetic, competes for a lysine from a preexisting salt bridge, initiating a partial unfolding event and promoting new protein interactions. Structural elements underlying the theft occurred early in evolution and are found in 10% of homo-oligomers and 30% of hetero-oligomers including Bax, Troponin C, and Early Endosome Antigen 1. In contrast to a direct recognition of phosphorylated residues by binding partners, the salt-bridge theft mechanism represents a facile strategy for promoting or disrupting protein interactions using solvent-accessible residues, and it can provide additional specificity at protein interfaces through local unfolding or conformational change.

Using hashes for user authentication allows systems to verify identity without storing or transmitting plaintext passwords, preventing theft or leakage. At the system level, access is granted if the hash matches and denied if it doesn't. A recent development in this area is chaos-based hashing, though it's not fully matured due to its complex and flawed design principles. This work proposes a novel chaos-based hash using a sponge construction. The design includes a four-state finite automaton to build the chaos structure, with a sponge mechanism for optimal bit mixing. Statistical evaluations show that the proposed hash offers strong diffusion, confusion, collision resistance, and balanced distribution. In addition to its provable security, it extends indifferentiability from random oracles in sponge-based constructions. Moreover, compared to existing chaos-based hashes, the proposed solution achieves superior performance.

The rapid development of deepfake technologies has led to the widespread proliferation of facial image forgeries, raising significant concerns over identity theft and the spread of misinformation. Although recent dual-domain detection approaches that integrate spatial and frequency features have achieved noticeable progress, they still suffer from limited sensitivity to local forgery regions and inadequate interaction between spatial and frequency information in practical applications. To address these challenges, we propose a novel forgery-aware guided spatial–frequency feature fusion network. A lightweight U-Net is employed to generate pixel-level saliency maps by leveraging structural symmetry and semantic consistency, without relying on ground-truth masks. These maps dynamically guide the fusion of spatial features (from an improved Swin Transformer) and frequency features (via Haar wavelet transforms). Cross-domain attention, channel recalibration, and spatial gating are introduced to enhance feature complementarity and regional discrimination. Extensive experiments conducted on two benchmark face forgery datasets, FaceForensics++ and Celeb-DFv2, show that the proposed method consistently outperforms existing state-of-the-art techniques in terms of detection accuracy and generalization capability. The future work includes improving robustness under compression, incorporating temporal cues, extending to multimodal scenarios, and evaluating model efficiency for real-world deployment.

The security and confidentiality of medical images are of utmost importance due to frequent issues such as leakage, theft, and tampering during transmission and storage, which seriously impact patient privacy. Traditional encryption techniques applied to entire images have proven to be ineffective in guaranteeing timely encryption and preserving the privacy of organ regions separated from the background. In response, this study proposes a specialized and efficient local image encryption algorithm for the medical field. The proposed encryption algorithm focuses on the regions of interest (ROI) within massive medical images. Initially, the Laplacian of Gaussian operator and the outer boundary tracking algorithm are employed to extract the binary image and achieve ROI edge extraction. Subsequently, the image is divided into ROI and ROB (regions outside ROI). The ROI is transformed into a row vector and rearranged using the Lorenz hyperchaotic system. The rearranged sequence is XOR with the random sequence generated by the Henon chaotic map. Next, the encrypted sequence is arranged according to the location of the ROI region and recombined with the unencrypted ROB to obtain the complete encrypted image. Finally, the least significant bit algorithm controlled by the key is used to embed binary image into the encrypted image to ensure lossless decryption of the medical images. Experimental verification demonstrates that the proposed selective encryption algorithm for massive medical images offers relatively ideal security and higher encryption efficiency. This algorithm addresses the privacy concerns and challenges faced in the medical field and contributes to the secure transmission and storage of massive medical images.

In the modern world, high-bandwidth internet access has led to the theft of all types of digital data, including digital video, and their illegal distribution. The digital video watermarking technique was introduced late last century to enforce video copyright protection. In this paper, a robust blind and secure video watermarking method is presented based on integer wavelet transform and the generalized chaotic sine map. In this method, integer wavelet transform is applied to each main frame of the standard video. Basically, watermarking techniques are evaluated based on three concepts of content quality, data resilience and data capacity. Therefore, in order to guarantee the quality of the watermarked video, the watermark is inserted in low-frequency coefficients. An appropriate security level is added to increase the efficiency and functionality by chaotic map to watermark. In addition, the normalized correlation coefficient (NCC) between the main watermark and extraction watermark is used as the main criterion of resistance measurement. The results show that the proposed method is good in terms of both quality and resistance to a variety of attacks, and the NCC values obtained in most cases are 1 or very close to 1.