Hash Code Generation Research Articles

Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics.

This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation of palmprint hash codes, optimizing for minimal storage as templates and efficient matching. The hashing branch derives the complementary information from these tasks by amalgamating knowledge acquired from the classification branch. This approach leads to superior overall performance compared to individual tasks in isolation. To enhance the effectiveness of multitask learning, two additional modules, an attention mechanism module and a customized gate control module, are introduced. These modules are vital in allocating higher weights to crucial channels and facilitating task-specific expert knowledge integration. Furthermore, an automatic weight adjustment module is incorporated to optimize the learning process further. This module fine-tunes the weights assigned to different tasks, improving performance. Integrating the three modules above has shown promising accuracies across various classification tasks and has notably improved authentication accuracy. The extensive experimental results validate the efficacy of our proposed framework.

Sequential and Correlated Image Hash Code Generation with Deep Reinforcement Learning

Image hashing is an algorithm used to represent an image with a unique value. Hashing methods, which are generally developed to search for similar examples of an image, have gained a new dimension with the use of deep network structures and better results have started to be obtained with the methods. The developed deep network models generally consider hash functions independently and do not take into account the correlation between them. In addition, most of the existing data-dependent hashing methods use pairwise/triplet similarity metrics that capture data relationships from a local perspective. In this study, the Central similarity metric, which can achieve better results, is adapted to the deep reinforcement learning method with sequential learning strategy, and successful results are obtained in learning binary hash codes. By taking into account the errors of previous hash functions in the deep reinforcement learning strategy, a new model is presented that performs interrelated and central similarity based learning.

Unsupervised soft-to-hard hashing with contrastive learning

In order to optimize a binary hash code with deep neural networks, greedy optimization with the discrete constraint relaxation is widely used due to its efficiency. However, noises from skipping gradients may destabilize the optimization in contrastive learning since the noises are superimposed on embedding from two distorted views. To reduce such undesirable impact from the noises, we propose a novel soft-to-hard hashing method by introducing an auxiliary loss in the penultimate block of the model based on the information bottleneck principle, which not only propagates the correlations between positives but also makes the embedding to be non-redundant to the samples in a high-dimensional continuous space. Our method beats the state-of-the-art result on three public datasets and demonstrates how soft code can help greedy back-propagation find a better solution during optimization. Another benefit of our method is that it enables a joint training of unsupervised representational learning and hash code generation, and obtains 5.4% mAP gain over conventional step-by-step training on the CIFAR-10 dataset.

A novel double keys adapted elliptic curve cryptography and log normalized Gaussian sigmoid adaptive neuro‐fuzzy interference system based secure resource allocation system in decentralized cloud storage

AbstractCloud computing has emerged as a promising platform that grants users direct yet shared access to computing resources and services without worrying about the internal complex infrastructure. However, secure data storage and data retrieval is the basic characteristics. Therefore, this paper proposes a novel double keys adapted elliptic curve cryptography (DKECC) and log normalized Gaussian sigmoid adaptive neuro‐fuzzy interference system (LGS‐ANFIS) based secure resource allocation system in decentralized cloud storage. Initially, the input data undergoes data fragmentation. After that, encryption is carried out using DKECC algorithm. Next, hash code generation using Pearson hash function is done. Then, resource availability is estimated via logistic sine chaotic mapping indulged rock hyraxes swarm optimization technique. Afterwards, data is checked for deduplication. Finally, resource allocation through LGS‐ANFIS takes place. The experimental outcomes demonstrated better results compared to baseline techniques. The modelling process showed that the suggested safe resource allocation model achieves the total security level of 96.27% while also having shorter reaction times (3018 ms), greater throughputs (1258), reduced load balancing (0.355874), and reduced latency (4372 ms).

Deep Supervised Cross-modal Hashing for Ship Image Retrieval

The retrieval of multimodal ship images obtained by remote sensing satellites is an important content of remote sensing data analysis, which is of great significance to improve the ability of marine monitoring. In this paper, We propose a novel cross-modal ship image retrieval method, called Deep Supervised Cross-modal Hashing(DSCMH). It consists of a feature learning part and a hash learning part used for feature extraction and hash code generation separately, both two parts have modality-invariant constraints to keep the cross-modal invariability, and the label information is also brought to supervise the above process. Furthermore, we design a class attention module based on the cross-modal class center to strengthen class discrimination. The experiment results show that the proposed method can effectively improve the cross-modal retrieval accuracy of ship images and is better than several state-of-the-art methods.

Deep hashing with self-supervised asymmetric semantic excavation and margin-scalable constraint

Due to its effectivity and efficiency, deep hashing approaches are widely used for large-scale visual search. However, it is still challenging to produce compact and discriminative hash codes for images associated with multiple semantics for two main reasons, 1) similarity constraints designed in most of the existing methods are based upon an oversimplified similarity assignment(i.e., 0 for instance pairs sharing no label, 1 for instance pairs sharing at least 1 label), 2) the exploration in multi-semantic relevance are insufficient or even neglected in many of the existing methods. These problems significantly limit the discrimination of generated hash codes. In this paper, we propose a novel Deep Hashing with Self-Supervised Asymmetric Semantic Excavation and Margin-Scalable Constraint(SADH) approach to cope with these problems. SADH implements a self-supervised network to sufficiently preserve semantic information in a semantic feature dictionary and a semantic code dictionary for the semantics of the given dataset, which efficiently and precisely guides a feature learning network to preserve multi-label semantic information using an asymmetric learning strategy. By further exploiting semantic dictionaries, a new margin-scalable constraint is employed for both precise similarity searching and robust hash code generation. Extensive empirical research on four popular benchmarks validates the proposed method and shows it outperforms several state-of-the-art approaches. The source codes URL of our SADH is:http://github.com/SWU-CS-MediaLab/SADH.

Label embedding semantic-guided hashing

Hashing technologies have been widely used for information retrieval tasks due to their efficient retrieval and storage capabilities. Generally, most of the current supervised learning only utilizes labels to construct a binary similarity matrix of instance pairs and ignores the rich semantic information contained in the labels. Indeed, the reason why supervised hashing is better than unsupervised hashing is that the labels itself has strong discriminative information. Therefore, how to effectively explore the label information is one of the ways to improve the performance of retrieval tasks. In addition, existing hashing methods have the problems of high time consumption and weak scalability when facing large-scale data. To remedy these problems, in this paper, we present a flexible two-step label embedding hashing method named Label Embedding Semantic-Guided Hashing (LESGH). In the first step, LESGH leverages an asymmetric discrete learning framework to learn discriminative compact hash codes only from label information, and adds the constraints of bit-balance and bit-decorrelation to boost the quality of the hash code generation. In the second step, LESGH learns the hash projection function through the generated hash codes in the first step. Moreover, an effective and fast iterative discrete optimization algorithm is presented to solve the discrete problem instead of using the relaxation-based scheme. In doing so, we can not only simplify the optimization process, but also easily scale to large-scale data. We conduct several experiments on three public datasets, i.e., WIKI, MIRFlickr and NUS-WIDE, demonstrate that LESGH can improve the retrieval performance over the compared state-of-the-art baselines.

Multisource Data Reconstruction-Based Deep Unsupervised Hashing for Unisource Remote Sensing Image Retrieval

Unsupervised hashing for remote sensing (RS) image retrieval first extracts image features and then use these features to construct supervised information (e.g., pseudo-labels) to train hashing networks. Existing methods usually regard RS images as natural images to extract unisource features. However, these features only contain partial information about ground objects and cannot produce reliable pseudo-labels. In addition, existing methods only generate a pseudo single-label to annotate each RS image, which cannot accurately represent multiple scenes in a RS image. To address these drawbacks, this paper proposes a new Multisource data reconstruction-based deep unsupervised Hashing method, called MrHash, which explores the characteristics of RS images to construct reliable pseudo-labels. In particular, we first use geographic coordinates to obtain different satellite images and develop a novel autoencoder network to extract multisource features from these images. Then pseudo multi-labels are designed to deal with the coexistence of multiple scenes in a single image. These labels are generated by a custom probability function with extracted multisource features. Finally, we propose a novel multi-semantic hash loss by using the Kull-back–Leibler (KL) divergence to preserve the semantic similarity of these pseudo multi-labels in Hamming space. Our newly developed MrHash only uses multisource images to construct supervised information, and hash code generation still relies on a unisource input image. Experiments on benchmark datasets clearly show the superiority of the proposed method over state-of-the-art baselines. https://github.com/sunyuxi/MrHash.

Unsupervised cross-media Hashing retrieval based on multi-head attention network

The cross-media Hash retrieval encodes different media data into a common binary Hash space, which can effectively measure the correlation between different modal samples. In order to further improve the retrieval performance, this paper proposes an unsupervised cross-media Hash retrieval method based on multi-head attention network. First, we use a multi-head attention network to generate a Hash code matrix, which makes the images and texts match better. Second, an auxiliary similarity matrix is constructed to integrate the original neighborhood information from different modalities. Through the collaborative learning of auxiliary similarity matrix and Hash code matrix, our method can capture the potential correlations between different modalities and within the same modality. In addition, we design two loss functions to train the model, and adopt strategies of batch normalization and replacing Hash code generation functions to optimize the model, which greatly improves the training speed of the model. Experiments on three datasets show that the average performance of our method is significantly higher than many state-of-the-art unsupervised methods, which fully proves the effectiveness and superiority of our method.

Matching images and texts with multi-head attention network for cross-media hashing retrieval

The cross-media hashing retrieval generally encodes multimedia data into a common binary hash space, which can effectively measure the correlation between samples from different modalities. However, in the cross-media retrieval, supervised methods require a lot of manual labels, which leads to the problem of high labor in practical application. Simultaneously, most unsupervised methods do not achieve good results by preserving the correlation between or within modalities. To attack these problems and further improve retrieval performance, this paper proposes an unsupervised cross-media hashing retrieval method based on multi-head attention network, which contains rich semantic information to match images and texts better. Specifically, we make use of a multi-head attention network for generating binary hash code better. At the same time, an auxiliary similarity matrix is constructed to integrate the original neighborhood information from different modalities. Therefore, this method can capture the potential relationships between inter-modal and intra-modal correlations. Furthermore, the method is unsupervised and requires no additional semantic labels, so it has the potential to achieve large-scale cross-media retrieval. In addition, two strategies of batch normalization and replacing hash code generation functions are adopted to optimize the model, and two loss functions are designed to make the performance of our method exceed that of many supervised cross-media hashing retrieval methods. Experiments on three baseline datasets show that our method performs much better than many state-of-the-art methods. The results demonstrate the effectiveness and superiority of our method.

Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval

Cross-modal hashing technology is a key technology for real-time retrieval of large-scale multimedia data in real-world applications. Although the existing cross-modal hashing methods have achieved impressive accomplishment, there are still some limitations: (1) some cross-modal hashing methods do not make full consider the rich semantic information and noise information in labels, resulting in a large semantic gap, and (2) some cross-modal hashing methods adopt the relaxation-based or discrete cyclic coordinate descent algorithm to solve the discrete constraint problem, resulting in a large quantization error or time consumption. Therefore, in order to solve these limitations, in this paper, we propose a novel method, named Discrete Semantics-Guided Asymmetric Hashing (DSAH). Specifically, our proposed DSAH leverages both label information and similarity matrix to enhance the semantic information of the learned hash codes, and the ℓ2,1 norm is used to increase the sparsity of matrix to solve the problem of the inevitable noise and subjective factors in labels. Meanwhile, an asymmetric hash learning scheme is proposed to efficiently perform hash learning. In addition, a discrete optimization algorithm is proposed to fast solve the hash code directly and discretely. During the optimization process, the hash code learning and the hash function learning interact, i.e., the learned hash codes can guide the learning process of the hash function and the hash function can also guide the hash code generation simultaneously. Extensive experiments performed on two benchmark datasets highlight the superiority of DSAH over several state-of-the-art methods.

Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval

It is very pleasing for human health that medical knowledge has increased and the technological infrastructure improves medical systems. The widespread use of medical imaging devices has been instrumental in saving lives by allowing early diagnosis of many diseases. These medical images are stored in large databases for many purposes. These datasets are used when a suspicious diagnostic case is encountered or to gain experience for inexperienced radiologists. To fulfill these tasks, images similar to one query image are searched from within the large dataset. Accuracy and speed are vital for this process, which is called content-based image retrieval (CBIR). In the literature, the best way to perform a CBIR system is by using hash codes. This study provides an effective hash code generation method based on feature selection-based downsampling of deep features extracted from medical images. Firstly, pre-hash codes of 256-bit length for each image are generated using a pairwise siamese network architecture that works based on the similarity of two images. Having a pre-hash code between -1 and 1 makes it very easy to generate hash code in hashing algorithms. For this reason, all activation functions of the proposed convolutional neural network (CNN) architecture are selected as hyperbolic tanh. Finally, neighborhood component analysis (NCA) feature selection methods are used to convert pre-hash code to binary hash code. This also downsamples the hash code length to 32-bit, 64-bit, or 96-bit levels. The performance of the proposed method is evaluated using NEMA MRI and NEMA CT datasets.

DAHP: Deep Attention-Guided Hashing With Pairwise Labels

To address the problem of inadequate feature extraction and binary code discrete optimization faced by deep hashing methods using a relaxation-quantization strategy, a novel deep attention-guided hashing method with pairwise labels (DAHP) is proposed to enhance global feature fusion, better learn the contextual information of image features to effectively enhance the feature representation, and solve the problem of losing feature information in discrete optimization by optimizing the loss function. First, we introduce a new concept called the anchor hash code generation(AHCG) algorithm, we train the ResNet with the position attention and channel attention mechanisms with the anchor points in Hamming space as supervised information, we fit the binary code representing the picture to the vicinity of each anchor point, and finally, we use the optimized loss function to calculate the pairwise loss and the anchor loss, allowing the hash function to generate hash code with strong discriminative power. The experiments were conducted on four benchmark datasets, and the retrieval accuracy of the proposed method outperformed the retrieval accuracies of the state-of-the-art methods.

Salience-Guided Iterative Asymmetric Mutual Hashing for Fast Person Re-Identification.

Person Re-identification (ReID) aims to retrieve the pedestrian with the same identity across different views. Existing studies mainly focus on improving accuracy, while ignoring their efficiency. Recently, several hash based methods have been proposed. Despite their improvement in efficiency, there still exists an unacceptable gap in accuracy between these methods and real-valued ones. Besides, few attempts have been made to simultaneously explicitly reduce redundancy and improve discrimination of hash codes, especially for short ones. Integrating Mutual learning may be a possible solution to reach this goal. However, it fails to utilize the complementary effect of teacher and student models. Additionally, it will degrade the performance of teacher models by treating two models equally. To address these issues, we propose a salience-guided iterative asymmetric mutual hashing (SIAMH) to achieve high-quality hash code generation and fast feature extraction. Specifically, a salience-guided self-distillation branch (SSB) is proposed to enable SIAMH to generate hash codes based on salience regions, thus explicitly reducing the redundancy between codes. Moreover, a novel iterative asymmetric mutual training strategy (IAMT) is proposed to alleviate drawbacks of common mutual learning, which can continuously refine the discriminative regions for SSB and extract regularized dark knowledge for two models as well. Extensive experiment results on five widely used datasets demonstrate the superiority of the proposed method in efficiency and accuracy when compared with existing state-of-the-art hashing and real-valued approaches. The code is released at https://github.com/Vill-Lab/SIAMH.

Enabling self-adaptability of small scale and large scale security systems using dynamic partial reconfiguration

The application areas of field programmable gate arrays (FPGAs) are increasing due to its hardware acceleration and reprogrammable features. From large-scale computation systems like cloud, aerospace, and defence to small-scale computation systems like home automation and mobile phones, the dynamic partial reconfiguration property is found to be attractive to design adaptive systems for self-reconfiguration and self-healing. The article presents two self- adaptive security systems for small scale as well as for large-scale systems. The security system is designed to include encryption accelerators and hash code generation accelerators. The security system designed for small-scale systems saves space and power using hardware adaptation by loading or creating only the required accelerator during execution of the application. It uses light weight cryptographic algorithms. A second design for large-scale systems focuses on getting more throughput by allocating more resources to the required accelerator at runtime. Two designs are created for hardware adaptation based on the accelerator requirement at runtime. Proposed adaptive design for small-scale systems achieved 68.50% decrease in resource consumption and design for large-scale systems achieved 25.93% more throughput than the best existing implementations.

FORMATION OF HASH CODES BASED ON THE UMAC ALGORITHM ON HYBRID CRYPTO-CODE CONSTRUCTIONS OF McELICE ON DAMAGED CODES

A study was carried out on the use of an improved UMAC algorithm in post-quantum cryptography based on the formation of a substrate on the third layer of the hash code generation by the McElise crypto-code system on elliptic codes. The paper considers a practical algorithm for generating a hash code based on an example implementation of a cascading UMAC hash algorithm with the McElise crypto-code construction on elliptic codes. Using a hybrid crypto-code design allows you to save the universality of the hash code at the output of the algorithm, which allows its use in large databases as an identifier. In addition, in the context of the implementation of a full-scale quantum computer, US NIST experts consider crypto-code systems as one of the effective post-quantum cryptography algorithms. This approach allows you to implement the UMAC modification on various modifications of hybrid crypto-code structures and to ensure the formation of authentication profiles of different strength and length.

Stacked auto-encoder based tagging with deep features for content-based medical image retrieval

Content-based medical image retrieval (CBMIR) is one of the most challenging and ambiguous tasks used to minimize the semantic gap between images and human queries in datasets with rich information content. Similar to the human visual saliency mechanism, CBMIR systems also use the visual features in the images for searching purposes. As a result of this search process, automatically accessing the images is very convenient in large and balanced datasets. Still, it is generally not possible to find such datasets in the medical domain. In this study, a four-step and effective hash code generation technique is presented to reduce the semantic gap between low-level features and high-level semantics for unbalanced medical image datasets. In the first stage, the convolutional neural network (CNN) architecture, the most effective feature representation method available today, is employed to extract discriminative features from images automatically. The features obtained in the last fully connected layer (FCL) at the output of the CNN architecture are used for hash code generation. In the second stage, using the Synthetic Minority Over-sampling Technique (SMOTE), the imbalance between the classes in the dataset is reduced. The solution to the unbalanced problem increases performance by almost 3%. In the third stage, balanced features are converted to a code of 13 symbols by using deep stacked auto-encoder. Finally, this code is translated to the standard 13-character labeling and retrieval code used by the 'Image retrieval in the medical application' (IRMA) dataset, since this is the database with which experiments have been done. IRMA error parameter, classification performance, and retrieval performance of the proposed method are more successful than other state-of-the-art methods.

Dynamic partial reconfiguration enchanced with security system for reduced area and low power consumption

Field-programmable gate arrays (FPGAs) have travelled far from just being utilized as glue logic to an entire system solution. This is mostly due to their generalized re-configurable nature, lower non-recurring engineering (NRE) expense, and also fast time to market. Owing to the reconfigurable nature of FPGA, a new field called reconfigurable computing that can change the circuit configuration after hardware production came into existence. Application of re-configurable computing for self-adaptive hardware allows hardware to get adapt to various environmental conditions and different needs by swapping or loading disparate computational modules. This work proposes an effectual design methodology (enhanced DPR security system (EDPRSS)) utilized to execute high performance FPGA device in respect of low power consumption along with security for the area reduction. In the proposed technique, hash code generation (HCG) and encryption hardware accelerators can well be dynamically produced on FPGA utilizing partial re-configuration as stated by the application requisites. The system is competent to swap in or swap out the equivalent hardware accelerator during run time, which in turn diminishes the power and area. Here, 2 re-configurable partitions are produced for encryption and also HCG algorithm. Experiential outcomes proved that the proposed technique proffers better performance when contrasted to the other conventional systems.

Unsupervised Remote Sensing Image Retrieval Using Probabilistic Latent Semantic Hashing

Unsupervised hashing methods have attracted considerable attention in large-scale remote sensing (RS) image retrieval, due to their capability for massive data processing with significantly reduced storage and computation. Although existing unsupervised hashing methods are suitable for operational applications, they exhibit limitations when accurately modeling the complex semantic content present in RS images using binary codes (in an unsupervised manner). To address this problem, in this letter, we introduce a novel unsupervised hashing method that takes advantage of the generative nature of probabilistic topic models to encapsulate the hidden semantic patterns of the data into the final binary representation. Specifically, we introduce a new probabilistic latent semantic hashing (pLSH) model to effectively learn the hash codes using three main steps: 1) data grouping, where the input RS archive is clustered into several groups; 2) topic computation, where the pLSH model is used to uncover highly descriptive hidden patterns from each group; and 3) hash code generation, where the data probability distributions are thresholded to generate the final binary codes. Our experimental results, obtained on two benchmark archives, reveal that the proposed method significantly outperforms state-of-the-art unsupervised hashing methods.

РЕКОМЕНДАЦІЇ ЩОДО ВИБОРУ СПОСОБУ БЕЗПЕЧНОГО ЗБЕРІГАННЯ ПАРОЛІВ

Using passwords remains the most common way to authenticate users for various types of information systems. This poses the challenge of securing the storage of user authentication information and protecting it from unauthorized access. In practice, various algorithms for secure password storage have become widespread. Mutually contradictory requirements for such algorithms for secure password storage, which on the one hand must be complex enough to coun ter various attacks, and on the other – simple to ensure the speed of the information system – determine the relevance of the study. There is a significant threat of direct search, especially given the fact that the computing power of CPUs and GPUs is constantly growing. Therefore, there is a need to be able to change the complexity of the hash code calcula tion, and therefore the amount of computation and time so as to significantly complicate the attack, but not cause dis comfort to the end user due to the delay in password verification. Among the known methods of secure password stor age are password encryption, the use of the hash function in the classical version, as well as the addition of salt and the use of iterations to calculate the hash code. The comparative analysis of the given methods is carried out in the work, their advantages and disadvantages are established, expedient areas of application of each method are outlined, the corresponding recommendations are developed. For the computational experiment, the tools of the Microsoft .NET Core 3.1 platform were used, which made it possible to set the time indicators of the hash code generation algorithm depending on the set parameters of the algorithm. The data obtained from the experiment can be used to select a method of securely storing passwords.