Molecular representations largely determine the learnability of quantum-chemical properties with machine learning. In order to find the most appropriate way to represent molecules in chemoinformatic studies, a comparative study of nine two-dimensional molecular fingerprints and three RDKit descriptor sets (PHYS, CONF, and PHCO) was conducted in terms of the prediction of five molecular properties by trained predictive machine learning models. The use of RDKit descriptor sets consistently yields more accurate results than hashed fingerprints across properties. Among fingerprints, Layered Fingerprint outperforms for global energy targets (Etot, Eee, Exc), whereas ECFP6 demonstrates better performance for atom-localized (Eatom) and thermodynamic targets (Cp). We further evaluate how the choice of hash function used during fingerprint construction affects representation quality and identify that noncryptographic hashing preserves locality and leads to better and more consistent outcomes than cryptographic hashing (SHA-256). This work provides mechanistic insights into how different molecular representations encode structural and physicochemical information, highlighting the merits and limits of descriptors for learning quantum-chemical properties. This offers practical guidance for selecting molecular representations and hashing strategies in designing and establishing pipelines for the artificial intelligence study of chemistry.

Vision-based food image retrieval has garnered significant attention due to its potential for critical applications in dietary and health management. However, food images exhibit more complex feature distributions and lack the geometric regularity and structured patterns typically observed in general image retrieval tasks. This complexity poses a challenge for existing models to extract fine-grained features and semantic information, thereby compromising retrieval performance. To address this challenge, we propose FoodHash, a context-aware proxy interaction and fusion hashing method for food image retrieval. The method incorporates an Aggregation-Interaction-Propagation (AIP) module that facilitates contextual information exchange among patch tokens within the same feature map, guided by proxy tokens, thereby effectively capturing the intricate details of food images. Furthermore, to leverage the rich semantic information in food images, a Cross-Fusion Module is introduced to efficiently integrate multi-scale information and enhance feature representation. Additionally, we employ a novel loss function to optimize hash learning by ensuring consistency between hash codes and the semantic space, thereby enhancing the learning capability of hash coding. Extensive experiments on three publicly available food datasets demonstrate that FoodHash significantly surpasses existing models in retrieval performance. Specifically, on the ETH Food-101 dataset, FoodHash achieves improvements of 18.1%, 6.7%, 5.2% and 4.5% over the suboptimal method PTLCH for 16-bits, 32-bits, 48-bits and 64-bits hash codes, respectively. The source code will be made publicly available upon publication of the paper.

QR codes have become one of the most commonly used technologies during the COVID-19 pandemic in government, business, and various organizations, with significant usage in financial systems for payments, authentication, and access to digital services. QR codes are primarily used as digital links to web applications or for contactless interactions. Due to the advancing nature of technology, QR technology has developed along with various risks, one innovative instance of this risk being QR code phishing. QR code phishing attacks lead to significant and harmful consequences, including monetary loss and data breaches, affecting both individuals and organizations, particularly in banking, mobile payment systems, and online financial services. QR codes can be compromised and directed to harmful websites, causing unsuspecting individuals to end up on deceptive financial platforms. Recently studied countermeasures reveal significant shortcomings, such as dependence on trusted third parties for verification, excessive time complexity, and the failure to guarantee confidentiality, integrity, authentication, and availability concurrently within one system. Therefore, a stronger countermeasure is necessary for effectively reducing QR code phishing attacks, especially in financial environments where security and trust in transactions are vital. Thus, this research focused on creating a blockchain-based framework designed to reduce QR phishing attacks. The application developed includes a QR code generator along with a blockchain creation feature. The application safely saves created QR codes in Base64 format, along with related attributes like URLs, owner details, comments, and hash values by employing a proof-of-work system. Additional research is necessary regarding the preservation of the established blockchain within a distributed network, as this would improve real-time verification systems that can reduce QR code phishing threats in financial frameworks.

Abstract Quantum communication enables secure information transmission and entanglement distribution, but these tasks are fundamentally limited by the capacities of quantum channels. While quantum repeaters can mitigate losses and noise, entanglement swapping via a central node is ineffective against the Pauli dephasing channel due to degradation from Bell-state measurements. This suggests that purifying distributed Bell states before entanglement swapping is necessary. Although one-way hashing codes are known to saturate the dephasing channel capacity, no explicit two-way purification protocol has previously been shown to achieve this bound. In this work, we present a two-way entanglement purification protocol with an explicit, scalable circuit that asymptotically achieves the dephasing channel capacity. With each iteration, the fidelity of Bell states increases. At the final round, the residual dephasing error is suppressed doubly-exponentially, scaling as Θ(p 2 n ), enabling near-perfect Bell pairs for any fixed number of purification rounds n. The explicit circuit we propose is versatile and applicable to any number of Bell pairs, offering a practical solution for mitigating decoherence in quantum networks and distributed quantum computing.

Planning forward: Deep incremental hashing by gradually defrosting bits.

A novel approach to generate cancellable biometric templates using style transfer and hash codes

Abstract Recent years have seen the widespread adoption of zkSNARKs constructed over small fields, including but not limited to, the Goldilocks field, small Mersenne prime fields, and tower of binary fields. Their appeal stems primarily from their efficacy in proving computations with small bit widths, which facilitates efficient proving of general computations and offers significant advantages, notably yielding remarkably fast proving efficiency for tasks such as proof of knowledge of hash preimages. Nevertheless, employing these SNARKs to prove algebraic statements (e.g., RSA, ECDSA signature verification) presents efficiency challenges, particularly in critical applications like zk-bridges and zkVMs that require verifying standard cryptographic primitives. To address this problem, we first define a new circuit model: arithmetic circuits with additional exponentiation gates . These gates serve as fundamental building blocks for establishing more intricate algebraic relations. Then we present a Hash-committed Commit-and-Prove (HCP) framework to construct Non-interactive Zero-knowledge (NIZK) proofs for the satisfiability of these circuits. Specifically, when proving knowledge of group exponentiations in discrete logarithm hard groups and RSA groups, compared to verifying complex group exponentiations within SNARK circuits, our approach requires proving only more lightweight computations within the SNARK, such as zk-friendly hash functions (e.g., Poseidon hash function). The number of these lightweight computations depends solely on the security parameter. This differentiation leads to substantial speedups for the prover relative to direct SNARK methods, while maintaining competitive proof size and verification cost.

MotivationRepresenting a set ofk-mers — strings of lengthk— in small space under fast lookup queries is a fundamental requirement for several applications in Bioinformatics. A data structure based onsparse and skew hashing(SSHash) was recently proposed for this purpose [Pibiri, 2022]: it combines good space effectiveness with fast lookup and streaming queries. It is alsoorder-preserving, i.e., consecutivek-mers (sharing a prefix-suffix overlap of lengthk–1) are assigned consecutive hash codes which helps compressing satellite data typically associated withk-mers, like abundances and color sets in colored De Bruijn graphs.ResultsWe study the problem of accelerating queries under the sparse and skew hashing indexing paradigm, without compromising its space effectiveness. We propose a refined data structure with less complex lookups and fewer cache misses. We give a simpler and faster algorithm for streaming lookup queries. Compared to indexes with similar capabilities and based on the Burrows-Wheeler transform, like SBWT and FMSI, SSHash is significantly faster to build and query. SSHash is competitive in space with the fast (and default) modality of SBWT when bothk-mer strands are indexed. While larger than FMSI, it is also more than one order of magnitude faster to query.Availability and ImplementationThe SSHash software is available athttps://github.com/jermp/sshash, and also distributed via Bioconda. A benchmark of data structures fork-mer sets is available athttps://github.com/jermp/kmer_sets_benchmark. The datasets used in this article are described and available athttps://zenodo.org/records/17582116.Contactgiulioermanno.pibiri@unive.it,rob@cs.umd.edu.

Linkable ring signatures are a type of ring signature scheme that can protect the anonymity of signers while allowing the public to verify whether the same signer has signed the same message multiple times. This functionality makes linkable ring signatures suitable for applications such as cryptocurrencies and anonymous voting systems, achieving the dual goals of identity privacy protection and misuse prevention. However, existing post-quantum linkable ring signature schemes often suffer from issues such as excessive linear data growth the adoption of post-quantum signature algorithms, and high circuit complexity resulting from the use of post-quantum zero-knowledge proof protocols. To address these issues, a logarithmic-size post-quantum linkable ring signature scheme based on aggregation operations is proposed. The scheme constructs a Merkle tree from ring members’ public keys via a hash algorithm to achieve logarithmic-scale signing and verification operations. Moreover, it introduces, for the first time, a post-quantum aggregate signature scheme to replace post-quantum zero-knowledge proof protocols, thereby effectively avoiding the construction of complex circuits. Scheme analysis confirms that the proposed scheme meets the correctness requirements of linkable ring signatures. In terms of security, the scheme satisfies the anonymity, unforgeability, and linkability requirements of linkable ring signatures. Moreover, the aggregation process does not leak information about the signing members, ensuring strong privacy protection. Experimental results demonstrate that, when the ring size scales to 1024 members, our scheme outperforms the existing Dilithium-based logarithmic post-quantum ring signature scheme, with nearly 98.25% lower signing time, 98.90% lower verification time, and 99.81% smaller signature size.

Securing visual information against sophisticated cyber threats remains a core challenge in modern cryptography because many existing chaos-based image encryption schemes suffer from low key sensitivity and static substitution. To overcome these intrinsic limitations, this study develops a multistage image encryption framework by synergistically fusing the Lorenz chaotic system, Secure Hash Algorithm 256 (SHA-256), and Discrete Time Quantum-inspired Walks (DTQWs). The chaotic Lorenz system yields highly sensitive diffusion sequences via bitwise modular operations, whereas the DTQW dynamically constructs plaintext-dependent Substitution Boxes (S-Boxes) and thereby reinforces confusion to minimize statistical predictability. The SHA-256 hash introduces a session-dependent quantum coin rotation parameter to ensure dynamic evolution with intrinsic plaintext sensitivity during the encryption process. Extensive simulations demonstrate outstanding security performance of the proposed scheme: near ideal entropy value of 7.9999, the Number of Pixels Change Rate (NPCR) and the Unified Average Intensity Value (UACI) rates of 99.6 % and 33.5%, correlation coefficients close to zero, and high decryption reconstruction fidelity with Peak Signal to Noise Ratio (PSNR = infty) and Normalized Correlation Coefficient (NCC = 1) for lossless recovery in our python based-evaluations. Compared with other state-of-the-art chaotic and quantum-inspired encryption techniques, the proposed framework offers superior randomness, a good diffusion-confusion balance, and robustness against statistical and differential attacks. Thus, it is a promising candidate for secure image communication and high-assurance data protection in next-generation multimedia systems.

Universal hash functions are a widely-used, fundamental building block in constructing more complex cryptographic schemes. This makes achieving high efficiency, both at the design and implementation level, an utmost priority. Using simple polynomial hash functions over prime fields is a popular choice; Poly1305 is a particular instance of such an approach that is standardized and widely deployed. However, even for simple polynomial hash functions, there are significant challenges in designing fast implementations. Firstly, there is a large set of choices for algorithmic parameters such as finite field and limb sizes. Secondly, the complexity and diversity of modern vector instruction set architectures (ISAs) makes performance evaluation, and subsequent parameter selection difficult. In this paper we present SPHGen, a program generator for simple polynomial hash functions. SPHGen takes as input the field parameters and outputs highly optimized code for a given vector ISA. The generated code is automatically verified by means of symbolic execution, ensuring functional correctness. Accompanying SPHGen is an accurate model that predicts the runtime of each generated program. Using SPHGen, one can readily identify the Pareto front of Pareto-optimal hash function parameters w.r.t. the security-performance trade-offs, and, when using the model, even without running any code. SPHGen and the model can be retargeted to different vector ISAs and languages; we consider AVX2, AVX512, AVX512_IFMA, and Jasmin as examples. We generate Jasmin code to ensure memory safety and constant-time execution. We report benchmarks showing that SPHGen offers significant performance improvements over the best previous non-vectorized code. In addition, for large messages, our automatically generated code offers speedups of up to 37% compared to the highly-optimized implementation of Poly1305 in OpenSSL, which is hand-coded in assembly.

This work studies the formation control problem of general linear multi-agent systems (MASs) under hybrid attacks that include man-in-the-middle attacks (MITM) and denial-of-service attacks (DoS). First, an improved Diffie–Hellman key exchange (DHKE)-based encryption–decryption mechanism is proposed. This mechanism combines the challenge–response mechanism and hash function, which can achieve identity authentication, detect MITM attacks and ensure the confidentiality and integrity of information. Second, considering that DoS attacks on different channels are independent, a division model for distributed DoS attacks is established, which can classify attacks into different patterns. Third, an edge-based event-triggered (ET) formation control scheme is proposed. This control method only relies on the information of neighbor agents, which not only saves communication resources but also resists distributed DoS attacks. Finally, sufficient conditions for the implementation of formation control for MASs under hybrid attacks are provided, and the effectiveness and advantages of the proposed strategy are verified by simulation.

This study proposes a fast image encryption method for color images, integrating an autoencoder to compress the image and a 6D hyperchaotic system to ensure enhanced security. Initially, a hash value is obtained from the original color image. The hash value, which serves as the secret key of the proposed encryption method, is used to initialize the state variables of the hyperchaotic system, which produces six distinct pseudo-random sequences. The input image is then compressed into a latent image (lossy) using a Vision Transformer Autoencoder model. This latent image is scrambled using chaotic sequences and a Random Shuffle technique. Diffusion is achieved through the Trifid Cipher transformation, which utilizes the remaining chaotic sequences to manipulate pixel values, thereby yielding a cipher version of the latent image. The suggested technique is faster and significantly enhances security compared to the state-of-the-art methods. This method achieves an average entropy of 7.9986, a correlation coefficient close to zero ≈ 0.00004, and key sensitivity analysis gives NPCR = 99.6110% and UACI = 33.4637%. Moreover, the key space of [Formula: see text] confirms that the proposed scheme offers strong resistance against brute-force attacks.

Biometric security technologies are increasingly important for protecting sensitive information and securing access control. There are inherent problems related to spoofing, privacy and data security in traditional monomial biometric systems. In this paper, we proposed a novel deep learning framework to enhance the biometrics security by using multispectral face, iris and fingerprint information. Combining deep hashing into the proposed fusion framework, a strong binary multimodal latent representation is generated which is robust in presence of fake attempts. The proposed approach also integrates a hybrid security framework (combining cancellable biometrics and secure sketch method) for improving security of biometric templates. Furthermore, deep auto encoder algorithm is applied for feature extraction to get improved encoded features in order to boast security. The efficacy of the approach is demonstrated on a multimodal face, iris and fingerprint biometric database, resulting in improved performance along with enhanced privacy through cancelability and unlink ability of biometrics templates. Deep hashing function is also tested on an image retrieval dataset task as well standard one where the network structure could be applied’re used and it shows similar adaptability.

Abstract Pseudo-random number generators (PRNGs) are essential for protecting secret information from unauthorized access. Elliptic curves (ECs) are widely used in cryptography due to its random and unpredictable nature. To balance the computational efficiency and security, this paper proposes a new PRNG named EC-SHA512 that integrates ECs with the cryptographic hash function SHA-512. Unlike existing approaches, our scheme: (i) generates an EC over a small finite field to provide a dynamic and adaptable base set; (ii) constructs the desired isomorphic sets from dynamically constructed isomorphic ECs instead of relying on a fixed EC; (iii) maps the acquired concatenated sets by applying SHA-512 to generate secure pseudo-random numbers (PRNs); (iv) using an affine transformation to reduce the structural patterns in the proposed sequences. The performance is evaluated by the widely known tests, including the suites of the National Institute of Standards and Technology (NIST), TestU01, and Diehard, and verified by an application in an image cryptosystem. The empirical results confirm the superior performance of the EC-SHA512 than the existing PRNGs. Moreover, it takes 0.59 second to generate a sequence of 10^6 bits, reducing the computational cost and providing high suitability for secure applications.

Dual aggregation based joint-modal similarity hashing for cross-modal retrieval.

As digital communication continues to expand, protecting the privacy and security of transmitted images has become increasingly crucial. Image hiding addresses this issue by concealing secret images within cover images. Most methods typically embed a secret image into a single cover image, which poses a risk if that cover image falls into the wrong hands. To address this issue, we propose a secret image hiding method that employs multiple cover images. The secret image is distributed across four cover images, while the fifth cover image is used to embed the perceptual hash values of the stego images in the proposed method. These hashes serve to accurately determine the correct sequence order of stego images during the extraction phase, especially in cases where file names or transmission order have been altered, ensuring proper extraction. Experimental results indicate that the proposed method achieves high visual quality of stego images, with an average PSNR of 44.9626 dB, and enables lossless recovery of the secret image.

This study presents a novel framework for digital copyright management that integrates AI-enhanced perceptual hashing, blockchain technology, and digital watermarking to address critical challenges in content protection and verification. Traditional watermarking approaches typically employ content-independent metadata and rely on centralized authorities, introducing risks of tampering and operational inefficiencies. The proposed system utilizes a pre-trained convolutional neural network (CNN) to generate a robust, content-based perceptual hash value, which serves as an unforgeable watermark intrinsically linked to the image content. This hash is embedded as a QR code in the frequency domain and registered on a blockchain, ensuring tamper-proof timestamping and comprehensive traceability. The blockchain infrastructure further enables verification of multiple watermark sequences, thereby clarifying authorship attribution and modification history. Experimental results demonstrate high robustness against common image modifications, strong discriminative capabilities, and effective watermark recovery, supported by decentralized storage via the InterPlanetary File System (IPFS). The framework provides a transparent, secure, and efficient solution for digital rights management, with potential future enhancements including post-quantum cryptography integration.

ABSTRACT In today's digital era, images play a vital role across diverse fields, including healthcare, banking, defence, traffic monitoring, and weather forecasting. As digital footprints, their use is rapidly growing, but they are also increasingly vulnerable to unauthorised access and misuse. To address this challenge, we propose a novel encryption scheme for multiple red, green, blue (RGB) images. The scheme takes an arbitrary number of images, overlays them to form a three‐dimensional (3D) image, and then divides it into four subparts. A five‐dimensional multi‐wing hyperchaotic map is employed for random selection of parts, images, rows, columns, and key images. Rows and columns from selected images are repeatedly swapped to produce scrambled 3D images, which are further XORed to generate the final encrypted outputs. The encrypted images are then combined into a large 3D RGB cipher image. To enhance security, the scheme integrates a 256‐bit salt key with SHA‐256 hash codes, ensuring strong key space and plaintext sensitivity. Experimental results demonstrate that the proposed approach provides robustness against multiple threats, real‐world applicability, and high security. Notably, the scheme achieved a highly competitive information entropy value of 7.99994, confirming its effectiveness. Extensive experiments further show that the ciphertexts exhibit high randomness and robustness: average correlation coefficients (CCs) between adjacent pixels are close to zero, the number of pixel change rate is 99.63%, and the unified average changing intensity is 33.45%. The decrypted images achieve peak signal‐to‐noise ratio (PSNR) values of ∞ (O–D) and 7.9982 (O–C), confirming lossless reconstruction. Moreover, the scheme demonstrates strong resistance to chosen plaintext as well as noise and cropping attacks, while maintaining competitive computational efficiency. Comparative analysis with recent chaos‐based algorithms verifies that the proposed approach provides superior security, randomness, and robustness for secure image transmission.

This study introduces a novel approach that integrates Gaussian Mixture Models (GMM) with MD5 hash-based verification to detect hidden messages embedded via Least Significant Bit (LSB) steganography in JPEG images. Unlike previous methods, the proposed dual-layer technique combines probabilistic modeling with data integrity verification. The model was trained and evaluated using a dataset comprising both original and stego-JPEG images. The experimental results achieved an accuracy of 78.67% and a precision of 89.15%, indicating good class separation between stego and non-stego images (AUC-ROC = 0.8659). However, the recall rate of 69.70% suggests that there is room for improvement in detecting all stego instances. Although MD5 is a hash function rather than an encryption algorithm, it effectively aids in identifying data anomalies resulting from message embedding. Overall, this lightweight approach offers a practical solution for steganalysis and can be further enhanced through the integration of hybrid deep learning techniques in future research.