A Review of Image Steganography Based on Multiple Hashing Algorithm

Steganography is a technique for hiding secret messages while sending and receiving communications through a cover item.From ancient times to the present,the security of secret or vital information has always been a significant problem.The development of secure communication methods that keep recipient-only data transmissions secret has always been an area of interest.Therefore,several approaches,including steganography,have been developed by researchers over time to enable safe data transit.In this review,we have discussed image steganography based on Discrete Cosine Transform(DCT)algorithm,etc.We have also discussed image steganography based on multiple hashing algorithms like the Rivest–Shamir–Adleman(RSA)method,the Blowfish technique,and the hash-least significant bit(LSB)approach.In this review,a novel method of hiding information in images has been developed with minimal variance in image bits,making our method secure and effective.A cryptography mechanism was also used in this strategy.Before encoding the data and embedding it into a carry image,this review verifies that it has been encrypted.Usually,embedded text in photos conveys crucial signals about the content.This review employs hash table encryption on the message before hiding it within the picture to provide a more secure method of data transport.If the message is ever intercepted by a third party,there are several ways to stop this operation.A second level of security process implementation involves encrypting and decrypting steganography images using different hashing algorithms.

基于word-hashing的DGA僵尸网络深度检测模型

针对使用域名生成算法(DGA)僵尸网络隐蔽性强,传统检测算法特征提取复杂的问题,提出一种无需提取具体特征的深度学习模型DGA域名检测方法.首先基于word-hashing将所有域名转用二元语法字符串表示,利用词袋模型把域名映射到高维向量空间.然后利用5层深度神经网络对转换为高维向量的域名进行训练分类检测.通过深度模型,能够从训练数据中发现不同层次抽象的隐藏模式和特征,而这些模式和特征使用传统的统计方法大多是无法发现的.实验中使用了10万条DGA域名和10万条合法域名作为样本,与基于自然语言特征分类算法进行对比实验.实验结果表明该深度模型对DGA域名检测准确率达到97.23%,比基于自然语言特征分类算法得到的检测准确率高3.7%.

Cross-Modal Hashing Retrieval Based on Deep Residual Network

In the era of big data rich inWe Media,the single mode retrieval system has been unable to meet people’s demand for information retrieval.This paper proposes a new solution to the problem of feature extraction and unified mapping of different modes:A Cross-Modal Hashing retrieval algorithm based on Deep Residual Network(CMHR-DRN).The model construction is divided into two stages:The first stage is the feature extraction of different modal data,including the use of Deep Residual Network(DRN)to extract the image features,using the method of combining TF-IDF with the full connection network to extract the text features,and the obtained image and text features used as the input of the second stage.In the second stage,the image and text features are mapped into Hash functions by supervised learning,and the image and text features are mapped to the common binary Hamming space.In the process of mapping,the distance measurement of the original distance measurement and the common feature space are kept unchanged as far as possible to improve the accuracy of Cross-Modal Retrieval.In training the model,adaptive moment estimation(Adam)is used to calculate the adaptive learning rate of each parameter,and the stochastic gradient descent(SGD)is calculated to obtain the minimum loss function.The whole training process is completed on Caffe deep learning framework.Experiments show that the proposed algorithm CMHR-DRN based on Deep Residual Network has better retrieval performance and stronger advantages than other Cross-Modal algorithms CMFH,CMDN and CMSSH.

Recent development of perceptual image hashing

The easy generation, storage, transmission and reproduction of digital images have caused serious abuse and security problems. Assurance of the rightful ownership, integrity, and authenticity is a major concern to the academia as well as the industry. On the other hand, efficient search of the huge amount of images has become a great challenge. Image hashing is a technique suitable for use in image authentication and content based image retrieval （CBIR）. In this article, we review some representative image hashing techniques proposed in the recent years, with emphases on how to meet the conflicting requirements of perceptual robustness and security. Following a brief introduction to some earlier methods, we focus on a typical two-stage structure and some geometric-distortion resilient techniques. We then introduce two image hashing approaches developed in our own research, and reveal security problems in some existing methods due to the absence of secret keys in certain stage of the image feature extraction, or availability of a large quantity of images, keys, or the hash function to the adversary. More research efforts are needed in developing truly robust and secure image hashing techniques.

基于直方图量化和混沌系统的感知图像Hashing算法

研究了基于图像灰度级压缩的直方图差值量化(DQH)技术,并结合混沌系统,提出了一种新的感知图像Hash ing算法。算法首先利用混沌系统把压缩后的图像中各个灰度级的出现概率调制成一个固定长度的中间Hash序列;然后将中间Hash序列经过差值量化和二值量化得到最终的图像Hash序列。仿真结果表明,该算法对JPEG压缩、低通滤波、图像缩放和旋转等操作有良好的鲁棒性,而且混沌系统的引入使算法具有较强的安全性。

Robust Image Hashing via Random Gabor Filtering and DWT

Image hashing is a useful multimedia technology for many applications,such as image authentication,image retrieval,image copy detection and image forensics.In this paper,we propose a robust image hashing based on random Gabor filtering and discrete wavelet transform(DWT).Specifically,robust and secure image features are first extracted from the normalized image by Gabor filtering and a chaotic map called Skew tent map,and then are compressed via a single-level 2-D DWT.Image hash is finally obtained by concatenating DWT coefficients in the LL sub-band.Many experiments with open image datasets are carried out and the results illustrate that our hashing is robust,discriminative and secure.Receiver operating characteristic(ROC)curve comparisons show that our hashing is better than some popular image hashing algorithms in classification performance between robustness and discrimination.

Fast Near-duplicate Image Detection in Riemannian Space by A Novel Hashing Scheme

There is a steep increase in data encoded as symmetric positive definite(SPD)matrix in the past decade.The set of SPD matrices forms a Riemannian manifold that constitutes a half convex cone in the vector space of matrices,which we sometimes call SPD manifold.One of the fundamental problems in the application of SPD manifold is to find the nearest neighbor of a queried SPD matrix.Hashing is a popular method that can be used for the nearest neighbor search.However,hashing cannot be directly applied to SPD manifold due to its non-Euclidean intrinsic geometry.Inspired by the idea of kernel trick,a new hashing scheme for SPD manifold by random projection and quantization in expanded data space is proposed in this paper.Experimental results in large scale nearduplicate image detection show the effectiveness and efficiency of the proposed method.

Feature Fusion Multi-View Hashing Based on Random Kernel Canonical Correlation Analysis

Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system,making it more and more widely used in image retrieval.Multi-view data describes image information more comprehensively than traditional methods using a single-view.How to use hashing to combine multi-view data for image retrieval is still a challenge.In this paper,a multi-view fusion hashing method based on RKCCA(Random Kernel Canonical Correlation Analysis)is proposed.In order to describe image content more accurately,we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT(Bag-of-Words model+SIFT feature)feature.This algorithm uses RKCCA method to fuse multi-view features to construct association features and apply them to image retrieval.The algorithm generates binary hash code with minimal distortion error by designing quantization regularization terms.A large number of experiments on benchmark datasets show that this method is superior to other multi-view hashing methods.

机群系统上基于Hashing的多目标串匹配并行算法

基于孙子定理构造均匀的Hash函数并继承Karp-Rabin模式匹配思想,利用"筛选"方法,给出一种机群系统上的多目标串匹配并行算法。通过预处理将字符串映射成惟一的一对整数值,采用比较一对整数值来取代逐个字符比较字符串的方法使得匹配过程快速且比较结果是确定的;"筛选"节省了比较时间。算法分析和实验结果表明该并行算法简明、高效和可扩展。

基于RESTful以及Salted Password Hashing算法的模拟试衣间系统

本文主要从系统设计、功能实现、整体架构的角度介绍了基于B/S模式下的模拟试衣间系统。本文还着重地介绍了Salted Password Hashing加密算法与传统的哈希函数加密算法之间存在的区别与联系,Salted Password Hashing加密算法在传统的软件系统中能够发挥重要作用,以及RESTful架构下软件系统的使用设计、实现方案及REST设计规范为实际软件编程带来的便利。

Lung Nodule Image Retrieval Based on Convolutional Neural Networks and Hashing

Lung medical image retrieval based on content similarity plays an important role in computer-aided diagnosis of lung cancer.In recent years,binary hashing has become a hot topic in this field due to its compressed storage and fast query speed.Traditional hashing methods often rely on highdimensional features based hand-crafted methods,which might not be optimally compatible with lung nodule images.Also,different hashing bits contribute to the image retrieval differently,and therefore treating the hashing bits equally affects the retrieval accuracy.Hence,an image retrieval method of lung nodule images is proposed with the basis on convolutional neural networks and hashing.First,apre-trained and fine-tuned convolutional neural network is employed to learn multilevel semantic features of the lung nodules.Principal components analysis is utilized to remove redundant information and preserve informative semantic features of the lung nodules.Second,the proposed method relies on nine sign labels of lung nodules for the training set,and the semantic feature is combined to construct hashing functions.Finally,returned lung nodule images can be easily ranked with the query-adaptive search method based on weighted Hamming distance.Extensive experiments and evaluations on the dataset demonstrate that the proposed method can significantly improve the expression ability of lung nodule images,which further validates the effectiveness of the proposed method.

Homomorphic Hashing Verification for Wireless Sensor Networks Rateless Codes Over-the-Air Programming

The homomorphic hash algorithm(HHA)is introduced to help on-the-fly verify the vireless sensor network(WSN)over-the-air programming(OAP)data based on rateless codes.The receiver calculates the hash value of a group of data by homomorphic hash function,and then it compares the hash value with the receiving message digest.Because the feedback channel is deliberately removed during the distribution process,the rateless codes are often vulnerable when they face security issues such as packets contamination or attack.This method prevents contaminating or attack on rateless codes and reduces the potential risks of decoding failure.Compared with the SHA1 and MD5,HHA,which has a much shorter message digest,will deliver more data.The simulation results show that to transmit and verify the same amount of OAP data,HHA method sends 17.9% to 23.1%fewer packets than MD5 and SHA1 under different packet loss rates.

Content-based image hashing using wave atoms

It is well known that robustness, fragility, and security are three important criteria of image hashing; however how to build a system that can strongly meet these three criteria is still a challenge. In this paper, a content-based image hashing scheme using wave atoms is proposed, which satisfies the above criteria. Compared with traditional transforms like wavelet transform and discrete cosine transform （DCT）, wave atom transform is adopted for the sparser expansion and better characteristics of texture feature extraction which shows better performance in both robustness and fragility. In addition, multi-frequency detection is presented to provide an application-defined trade-off. To ensure the security of the proposed approach and its resistance to a chosen-plaintext attack, a randomized pixel modulation based on the Rdnyi chaotic map is employed, combining with the nonliner wave atom transform. The experimental results reveal that the proposed scheme is robust against content-preserving manipulations and has a good discriminative capability to malicions tampering.

Orientation Field Code Hashing:A Novel Method for Fast Palmprint Identification

For a large-scale palmprint identification system,it is necessary to speed up the identification process to reduce the response time and also to have a high rate of identification accuracy.In this paper,we propose a novel hashing-based technique called orientation field code hashing for fast palmprint identification.By investigating hashing-based algorithms,we first propose a double-orientation encoding method to eliminate the instability of orientation codes and make the orientation codes more reasonable.Secondly,we propose a window-based feature measurement for rapid searching of the target.We explore the influence of parameters related to hashing-based palmprint identification.We have carried out a number of experiments on the Hong Kong Poly U large-scale database and the CASIA palmprint database plus a synthetic database.The results show that on the Hong Kong Poly U large-scale database,the proposed method is about 1.5 times faster than the state-of-the-art ones,while achieves the comparable identification accuracy.On the CASIA database plus the synthetic database,the proposed method also achieves a better performance on identification speed.

An Efficient Encrypted Speech Retrieval Based on Unsupervised Hashing and B+ Tree Dynamic Index

Existing speech retrieval systems are frequently confronted with expanding volumes of speech data.The dynamic updating strategy applied to construct the index can timely process to add or remove unnecessary speech data to meet users’real-time retrieval requirements.This study proposes an efficient method for retrieving encryption speech,using unsupervised deep hashing and B+ tree dynamic index,which avoid privacy leak-age of speech data and enhance the accuracy and efficiency of retrieval.The cloud’s encryption speech library is constructed by using the multi-threaded Dijk-Gentry-Halevi-Vaikuntanathan(DGHV)Fully Homomorphic Encryption(FHE)technique,which encrypts the original speech.In addition,this research employs Residual Neural Network18-Gated Recurrent Unit(ResNet18-GRU),which is used to learn the compact binary hash codes,store binary hash codes in the designed B+tree index table,and create a mapping relation of one to one between the binary hash codes and the corresponding encrypted speech.External B+tree index technology is applied to achieve dynamic index updating of the B+tree index table,thereby satisfying users’needs for real-time retrieval.The experimental results on THCHS-30 and TIMIT showed that the retrieval accuracy of the proposed method is more than 95.84%compared to the existing unsupervised hashing methods.The retrieval efficiency is greatly improved.Compared to the method of using hash index tables,and the speech data’s security is effectively guaranteed.

Secure Content Based Image Retrieval Scheme Based on Deep Hashing and Searchable Encryption

To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security,retrieval efficiency,and retrieval accuracy.This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure,searchable encryption scheme.First,a deep learning framework based on residual network and transfer learn-ing model is designed to extract more representative image deep features.Secondly,the central similarity is used to quantify and construct the deep hash sequence of features.The Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and low-complexity searchable index.Finally,according to the additive homomorphic property of Paillier homomorphic encryption,a similarity measurement method suitable for com-puting in the retrieval system’s security is ensured by the encrypted domain.The experimental results,which were obtained on Web Image Database from the National University of Singapore(NUS-WIDE),Microsoft Common Objects in Context(MS COCO),and ImageNet data sets,demonstrate the system’s robust security and precise retrieval,the proposed scheme can achieve efficient image retrieval without revealing user privacy.The retrieval accuracy is improved by at least 37%compared to traditional hashing schemes.At the same time,the retrieval time is saved by at least 9.7%compared to the latest deep hashing schemes.

Improved locality-sensitive hashing method for the approximate nearest neighbor problem

In recent years, the nearest neighbor search （NNS） problem has been widely used in various interesting applications. Locality-sensitive hashing （LSH）, a popular algorithm for the approximate nearest neighbor problem, is proved to be an efficient method to solve the NNS problem in the high-dimensional and large-scale databases. Based on the scheme of p-stable LSH, this paper introduces a novel improvement algorithm called randomness-based locality-sensitive hashing （RLSH） based on p-stable LSH. Our proposed algorithm modifies the query strategy that it randomly selects a certain hash table to project the query point instead of mapping the query point into all hash tables in the period of the nearest neighbor query and reconstructs the candidate points for finding the nearest neighbors. This improvement strategy ensures that RLSH spends less time searching for the nearest neighbors than the p-stable LSH algorithm to keep a high recall. Besides, this strategy is proved to promote the diversity of the candidate points even with fewer hash tables. Experiments are executed on the synthetic dataset and open dataset. The results show that our method can cost less time consumption and less space requirements than the p-stable LSH while balancing the same recall.

An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing

In this paper, we propose a new online system that can quickly detect malicious spam emails and adapt to the changes in the email contents and the Uniform Resource Locator (URL) links leading to malicious websites by updating the system daily. We introduce an autonomous function for a server to generate training examples, in which double-bounce emails are automatically collected and their class labels are given by a crawler-type software to analyze the website maliciousness called SPIKE. In general, since spammers use botnets to spread numerous malicious emails within a short time, such distributed spam emails often have the same or similar contents. Therefore, it is not necessary for all spam emails to be learned. To adapt to new malicious campaigns quickly, only new types of spam emails should be selected for learning and this can be realized by introducing an active learning scheme into a classifier model. For this purpose, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with a data selection function. In RAN-LSH, the same or similar spam emails that have already been learned are quickly searched for a hash table in Locally Sensitive Hashing (LSH), in which the matched similar emails located in “well-learned” are discarded without being used as training data. To analyze email contents, we adopt the Bag of Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency (TF-IDF). We use a data set of double-bounce spam emails collected at National Institute of Information and Communications Technology (NICT) in Japan from March 1st, 2013 until May 10th, 2013 to evaluate the performance of the proposed system. The results confirm that the proposed spam email detection system has capability of detecting with high detection rate.

Parallelized Hashing via <i>j</i>-Lanes and <i>j</i>-Pointers Tree Modes, with Applications to SHA-256

j-lanes tree hashing is a tree mode that splits an input message into?j?slices, computes?j?independent digests of each slice, and outputs the hash value of their concatenation.?j-pointers tree hashing is a similar tree mode that receives, as input,?j?pointers to?j?messages (or slices of a single message), computes their digests and outputs the hash value of their concatenation. Such modes expose parallelization opportunities in a hashing process that is otherwise serial by nature. As a result, they have a performance advantage on modern processor architectures. This paper provides precise specifications for these hashing modes, proposes appropriate IVs, and demonstrates their performance on the latest processors. Our hope is that it would be useful for standardization of these modes.

A <i>j</i>-Lanes Tree Hashing Mode and <i>j</i>-Lanes SHA-256

j-lanes hashing is a tree mode that splits an input message to j slices, computes j independent digests of each slice, and outputs the hash value of their concatenation. We demonstrate the performance advantage of j-lanes hashing on SIMD architectures, by coding a 4-lanes-SHA-256 implementation and measuring its performance on the latest 3rd Generation IntelR CoreTM. For messages whose lengths range from 2 KB to 132 KB, we show that the 4-lanes SHA-256 is between 1.5 to 1.97 times faster than the fastest publicly available implementation that we are aware of, and between ~2 to ~2.5 times faster than the OpenSSL 1.0.1c implementation. For long messages, there is no significant performance difference between different choices of j. We show that the 4-lanes SHA-256 is faster than the two SHA3 finalists (BLAKE and Keccak) that have a published tree mode implementation. Finally, we explain why j-lanes hashing will be faster on the coming AVX2 architecture that facilitates using 256 bits registers. These results suggest that standardizing a tree mode for hash functions (SHA-256 in particular) could be useful for performance hungry applications.