Hash Sequence Research Articles

A Robust Coverless Steganography Scheme Using Camouflage Image

Recently, most coverless image steganography (CIS) methods are based on robust mapping rules. However, due to the limited mapping expression relationship between secret information and hash sequence, it is a challenge to further improve the hiding ability of coverless information hiding. Towards this goal, this paper proposes a robust coverless steganography scheme using camouflage image(CI-CIS). For the sender, CI-CIS introduces an camouflage image as the transmission carrier and establishes the correlation between them by Convolutional Neural Network(CNN) features. For the receiver, the camouflage image can retrive the corresponding stego-image to recover the secret information. To this end, we designed a reversible retrieval scheme between stego-image and camouflage image by using image clustering. At the same time, since the semantic features represented by CNN are robust to image attacks, our method can increase the capability of the CIS effectively. Besides, we also build an inverted index to improve retrieval efficiency. Experimental results and analysis show that the CI-CIS has higher robustness and more flexible capacity setting compared with the existing CIS methods.

Coverless Video Steganography Based on Frame Sequence Perceptual Distance Mapping

Most existing coverless video steganography algorithms use a particular video frame for information hiding. These methods do not reflect the unique sequential features of video carriers that are different from image and have poor robustness. We propose a coverless video steganography method based on frame sequence perceptual distance mapping. In this method, we introduce Learned Perceptual Image Patch Similarity (LPIPS) to quantify the similarity between consecutive video frames to obtain the sequential features of the video. Then we establish the relationship map between features and the hash sequence for information hiding. In addition, the MongoDB database is used to store the mapping relationship and speed up the index matching speed in the information hiding process. Experimental results show that the proposed method exhibits outstanding robustness under various noise attacks. Compared with the existing methods, the robustness to Gaussian noise and speckle noise is improved by more than 40%, and the algorithm has better practicability and feasibility.

Perceptual Image Hashing Based on Multitask Neural Network

With the advent of the era of multimedia and in-depth development, the whole human society has been produced and spread a huge amount of image data, but at the same time, in view of the digital image and tamper with the attack of piracy phenomenon also more and more serious, malicious attacks will produce serious social, military, and political influence, therefore, to protect the authenticity of the original image content, which is also more and more important. In order to further improve the performance of image hashing and enhance the protection of image data, we proposed an end-to-end dual-branch multitask neural network based on VGG-19 to produce a perceptual hash sequence and used prepart of network of pretrained VGG-19 model to extract image features, and then, the image features are transformed into a hash sequence through a convolutional and fully connected network. At the same time, in order to enhance the function of the network and improve the adaptability of the proposed network to using scenarios, the rest part of the network layer of the VGG-19 model was used as another branch for image classification, so as to realize the multitask characteristics of the network. Through the experiment of the testing set, the network can not only resist many kinds of attack operations (content retention operations), but also realize accurate classification about the image, and has a satisfactory tampering detection ability.

Speech biohashing authentication based on power spectrum

In order to solve hashing sequences leakage in the existing speech authentication algorithms, this paper proposed a speech biohashing authentication based on power spectrum. Fristly, the algorithm pre-processed the original speech, then used the improved covariance method to extract the power spectrum feature of the speech, and generated a one-dimensional feature vector from the feature matrix. A pseudo-random matrix was generated by using Sinc function transform, the feature vector and pseudo-random matrix are projected and mapped to construct the biometric template. Finally, binarization process was used to generate hash index, and upload it to the cloud. The experimental results show that the algorithm has good discrimination, robustness and authentication performance. At the same time, the biometric template has good security and ensures the irreversibility of the hashing sequence.

Robust Image Hashing Based on Cool and Warm Hue and Space Angle

Image hashing has attracted more and more attention in the field of information security. In this paper, a novel hashing algorithm using cool and warm hue information and three-dimensional space angle is proposed. Firstly, the original image is preprocessed to get the opposite color component and the hue component H in HSV color space. Then, the distribution of cool and warm hue pixels is extracted from hue component H. Blocks the hue component H, according to the proportion of warm hue and cool hue pixels in each small block, combined with the quaternion and opposite color component, constructed the cool and warm hue opposite color quaternion (CWOCQ) feature. Then, three-dimensional space, opposite color, and cool and warm hue are combined to obtain the three-dimensional space angle (TDSA) feature. The CWOCQ feature and the TDSA feature are connected and disturbed to obtain the final hash sequence. Experimental results show that the proposed algorithm has good security and has better image classification performance and shorter computation time compared with some advanced algorithms.

Coverless Steganography Based on Motion Analysis of Video

With the rapid development of interactive multimedia services and camera sensor networks, the number of network videos is exploding, which has formed a natural carrier library for steganography. In this study, a coverless steganography scheme based on motion analysis of video is proposed. For every video in the database, the robust histograms of oriented optical flow (RHOOF) are obtained, and the index database is constructed. The hidden information bits are mapped to the hash sequences of RHOOF, and the corresponding indexes are sent by the sender. At the receiver, through calculating hash sequences of RHOOF from the cover video, the secret information can be extracted successfully. During the whole process, the cover video remains original without any modification and has a strong ability to resist steganalysis. The capacity is investigated and shows good improvement. The robustness performance is prominent against most attacks such as pepper and salt noise, speckle noise, MPEG-4 compression, and motion JPEG 2000 compression. Compared with the existing coverless information hiding schemes based on images, the proposed method not only obtains a good trade-off between hiding information capacity and robustness but also can achieve higher hiding success rate and lower transmission data load, which shows good practicability and feasibility.

An encrypted speech authentication and tampering recovery method based on perceptual hashing

With the progress of speech retrieval technology in the cloud, it brings a lot of conveniences for speech user. Yet, the inquiry encrypted speech results from the speech retrieval system are faced with some secure issues to settle, such as integrity authentication and tampering recovery. In this paper, an encrypted speech authentication and tampering recovery method based on perceptual hashing is proposed. Firstly, the original speech is scrambled by Duffing mapping to construct an encrypted speech library in the cloud, through extracting product of uniform sub-band spectrum variance and spectral entropy of encrypted speech and constructing a perceptual hashing sequence to generate the hashing template of the cloud. From this, a one-to-one correspondence between the encrypted speech and perceptual hashing sequence is established. Secondly, the authentication digest of encrypted speech is extracted according to the inquiry result during the retrieval. Then, the authentication digest and the perceptual hashing sequence of the hashing template in the cloud are matched by the Hamming distance algorithm. Finally, for encrypted speech that fails authentication, tampering detection and location are performed, and the tampered samples are recovered by the least square curve fitting method. The simulation results show that the proposed method can extract the authentication digest directly in the encrypted speech, and the authentication digest not only has good discrimination and robustness, but it accurately locates the tampered area for malicious substitution and mute attacks. In addition, the proposed method can recover tampered speech signals in high quality without any extra information.

A novel color image encryption algorithm based on image hashing, 6D hyperchaotic and DNA coding

In order to improve the key space of color image encryption algorithm, the sensitivity to the contents of plain images, the robustness against various types of known attacks, and to achieve the tamper location analysis, a novel color image encryption algorithm based on image hashing, six-dimensional (6D) hyperchaotic and dynamic DNA coding is proposed. Firstly, the color image is pre-processed and the hash sequence is extracted by image hashing algorithm which is used as the initial value and control parameter of chaotic system. Secondly, three color channels of the color image RGB are synthesized into a two-dimensional matrix and the pixels replacement are performed by using the improved two-dimensional chaotic map. Finally, a 6D hyperchaotic system is used to generate random sequences for DNA dynamic coding and arithmetic operations of color images, the encrypted image is obtained. The experimental results show that, compared with the existing methods, the proposed algorithm has a large enough key space, better plain-image sensitivity, better statistical and differential characteristics, as well as can resistant various forms of attacks such as noise and cropping, and the tampering image can be tamper-located analyzed, which has good security and strong robustness.

A New Robust Reference Image Hashing System

The authentication and content protection of multimedia data is a challenging task in the present scenario. One solution is to generate the perceptual hash which essentially authenticates the multimedia data and can also be dealt with image database search problems. In this article, a novel system for generating an image hash is presented. The proposed system utilizes the global and local features in the hash generation process. The local features are obtained from non-linear scale-space based KAZE features. These KAZE features have the ability to capture the most stable point under several content preserving distortions. In contrast, first the input image is converted to a normalized image, it is transformed to the log-polar coordinate system, and then a reference image using the local contrast in the wavelet domain is obtained to extrcat the global features. An intermediate hash sequence is achieved by extracting the significant information from the reference image using the singular value decomposition. The final hash sequence combines both the vectors followed by the randomization process. Extensive experiments are conducted to demonstrate the feasibility and the robustness of the proposed hashing system against a wide range of intentional/unintentional distortions. Further, the comparative analysis with some state-of-the-art techniques validates the better discrimination of the proposed work.

Image Hashing Algorithm Based on Mirror Flipping and a Three‐Dimensional Space Angle

In order to make full use of image flipping information to get comprehensive image features and improve the distinguishing performance of hash algorithm, this paper proposes a new image hashing algorithm based on mirror flipping and a three‐dimensional space angle. Firstly, the original image is preprocessed and then combined with mirror flipping image to obtain the new luminance component and opposite color components. Then, we combine new luminance component with the different sizes of structural elements to construct morphological features. The new opposite color components are used to construct a three‐dimensional space. The angle between vectors formed by the pixels in the three‐dimensional space is computed to construct the space angle features. Finally, the morphological features and space angle features are combined and disturbed to form the final hash sequence. Simulation results show that the algorithm has good security and tamper image recognition accuracy. Compared with some existing algorithms, it has better image classification performance and shorter computation time.

A Novel Comprehensive Watermarking Scheme for Color Images

Watermarking technology is commonly used to solve various problems in digital rights management and multimedia security. If a watermarking scheme with multiple purposes applies single method, it will easily cause the destruction of the hidden messages in particular attacks. For the copyright protection and tamper detection of color images, this research proposed a robust-fragile watermarking scheme. The two different embedding schemes embed the watermark into the R layer and G layer after NSST (nonsubsampled shearlet transform) and DWT (discrete wavelet transform) transformation. The hash sequence generated by the R layer and the G layer is served as fragile watermarks and is embedded into the B layer by the LSB (least significant bit) method. Finally, an improved rotation correction is applied to better extract the watermark under the rotation attack. Experimental results show that the proposed method is more accurate than the existing ones in terms of rotation angle correction and can effectively resist general attacks such as noise, filtering, and JEPG compression. Moreover, the proposed fragile watermark can locate the tamper position when malicious tamper occurs. Except cropping attack, the true-positive rate (TPR) reaches 1 for all attacks.

Perceptual Image Hashing for Content Authentication Based on Convolutional Neural Network With Multiple Constraints

In this paper, a novel perceptual image hashing scheme based on convolutional neural network (CNN) with multiple constraints is proposed, in which our deep hashing network learns the process of features extraction automatically according to the training target and then generates the final hash sequence. The combination of convolutional and pooling layers is to reduce the size of input image while deepening the channels. Then, we construct two pairs of constraints and integrate them into an overall constraint function through a strategy of weight allocation. In order to guarantee the robustness and discrimination of deep hashing network simultaneously, a new training method is developed to adjust the training set structure dynamically according to the changes of constraint values. Experimental results show that the proposed deep hashing network can achieve a satisfactory balance between perceptual robustnzess and discrimination while maintaining security. Based on the large-scale test set, receiver operating characteristic (ROC) curves, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}_{1}$ </tex-math></inline-formula> scores and equal error rate (EER) demonstrate the superiority of our scheme in terms of content authentication compared with some state-of-the-art schemes.

Perceptual Hashing With Visual Content Understanding for Reduced-Reference Screen Content Image Quality Assessment

Numerous screen content images (SCIs) have been produced to meet the needs of virtual desktop and remote display, which put forward a very urgent requirement for security and management of SCIs. Perceptual hashing is an effective way to deal with this issue. However, since SCIs are generally composed of pictures, graphics and texts, their intrinsic characteristics are different from those of natural images. Thus the previous hashing methods for natural images are not suitable for SCIs. In this article, we propose a perceptual hashing method for SCIs from the perspective of visual content understanding. Specifically, considering that the visual content understanding of SCIs mainly comes from textual regions, while the contours of text always have thinner width and higher contrast, it is decided to generate hash in the gradient field. An input screen image is first performed by some joint preprocessing operations. Then the maximum gradient magnitude and corresponding orientation information are extracted from three color channels R, G and B. Normalized histogram and local frequency coefficient features are further obtained from the maximum gradient magnitude. Finally, a hash sequence is constructed by statistics that are derived from extracted features. Experiments validated on three SCIs databases were conducted to evaluate classification between robustness and discrimination. Receiver operating characteristics (ROC) results demonstrate that the proposed method is superior to the state-of-the-art algorithms. Besides, SIQAD and SCID databases were leveraged to present the application in reduced-reference screen content image quality assessment, and comparisons show that our hashing could provide accurate predictions than other metrics.

Coverless image steganography based on DenseNet feature mapping

Since the concept of coverless information hiding was proposed, it has been greatly developed due to its effectiveness of resisting the steganographic tools. Most existing coverless image steganography (CIS) methods achieve excellent robustness under non-geometric attacks. However, they do not perform well under some geometric attacks. Towards this goal, a CIS algorithm based on DenseNet feature mapping is proposed. Deep learning is introduced to extract high-dimensional CNN features which are mapped into hash sequences. For the sender, a binary tree hash index is built to accelerate index speed of searching hidden information and DenseNet hash sequence, and then, all matched images are sent. For the receiver, the secret information can be recovered successfully by calculating the DenseNet hash sequence of the cover image. During the whole steganography process, the cover images remain unchanged. Experimental results and analysis show that the proposed scheme has better robust compared with the state-of-the-art methods under geometric attacks.

An efficient retrieval approach for encrypted speech based on biological hashing and spectral subtraction

In this paper, an efficient retrieval approach for encrypted speech based on biological hashing and spectral subtraction is proposed. The proposed approach improves the impact of noise on the robustness and discrimination of speech hashing scheme, as well as improves the retrieval efficiency, accuracy, and security of search digests, and realizes the authentication of the query result. The speech owner firstly secure the original speech file by encrypting it with two-dimensional Arnold mapping and uploading it to the encrypted speech library on cloud. Then, the pre-processed speech signal is subjected to spectral subtraction and noise reduction, as well as the discrete wavelet transform (DWT) is performed to obtain the wavelet low-frequency coefficient and reconstruct the speech signal, calculating the normalized autocorrelation function to obtain the matrix feature vector, and using the Chebychew mapping algorithm to generate the pseudo-random matrix, and generate the pseudo-random Fourier matrix by fast Fourier transform (FFT). Finally, iterate the matrix feature vector and pseudo-random matrix. After the thresholding, the hash sequence is constructed and uploaded to the system hash index table on cloud. When speech’s user retrieval, the Hamming distance algorithm is used for the matching retrieval operation during the search and integrity authentication of the query result. The experimental results show that the proposed approach effectively reduces the noise of speech, with strong robustness and discrimination, and the retrieval efficiency, accuracy and security have been significantly improved.

A Subject-Sensitive Perceptual Hash Based on MUM-Net for the Integrity Authentication of High Resolution Remote Sensing Images

Data security technology is of great significance to the application of high resolution remote sensing image (HRRS) images. As an important data security technology, perceptual hash overcomes the shortcomings of cryptographic hashing that is not robust and can achieve integrity authentication of HRRS images based on perceptual content. However, the existing perceptual hash does not take into account whether the user focuses on certain types of information of the HRRS image. In this paper, we introduce the concept of subject-sensitive perceptual hash, which can be seen as a special case of conventional perceptual hash, for the integrity authentication of HRRS image. To achieve subject-sensitive perceptual hash, we propose a new deep convolutional neural network architecture, named MUM-Net, for extracting robust features of HRRS images. MUM-Net is the core of perceptual hash algorithm, and it uses focal loss as the loss function to overcome the imbalance between the positive and negative samples in the training samples. The robust features extracted by MUM-Net are further compressed and encoded to obtain the perceptual hash sequence of HRRS image. Experiments show that our algorithm has higher tamper sensitivity to subject-related malicious tampering, and the robustness is improved by about 10% compared to the existing U-net-based algorithm; compared to other deep learning-based algorithms, this algorithm achieves a better balance between robustness and tampering sensitivity, and has better overall performance.

CNN Feature-Based Image Copy Detection with Contextual Hash Embedding

As one of the important techniques for protecting the copyrights of digital images, content-based image copy detection has attracted a lot of attention in the past few decades. The traditional content-based copy detection methods usually extract local hand-crafted features and then quantize these features to visual words by the bag-of-visual-words (BOW) model to build an inverted index file for rapid image matching. Recently, deep learning features, such as the features derived from convolutional neural networks (CNN), have been proven to outperform the hand-crafted features in many applications of computer vision. However, it is not feasible to directly apply the existing global CNN features for copy detection, since they are usually sensitive to partial content-discarded attacks, such as copping and occlusion. Thus, we propose a local CNN feature-based image copy detection method with contextual hash embedding. We first extract the local CNN features from images and then quantize them to visual words to construct an index file. Then, as the BOW quantization process decreases the discriminability of these features to some extent, a contextual hash sequence is captured from a relatively large region surrounding each CNN feature and then is embedded into the index file to improve the feature’s discriminability. Extensive experimental results demonstrate that the proposed method achieves a superior performance compared to the related works in the copy detection task.

Restoring the Order of Information Packets Based on Hash Sequence Analysis

Purpose of research. Currently, various technologies and methods are used to control the integrity and authenticity of data transmitted through open communication channels. One of them is the technology for transmitting sequences of information packets connected to each other in chains using certain cryptographic algorithms. Similar approaches are used in the well-known blockchain technology and are focused on large volumes of transmitted and protected information and large sizes of additional service information fields. The purpose of this article is to study the characteristics of systems, transmission of small information packets in comparison with traditional size frames of TCP/IP stack, in which the broken packet sequence order is restored using the chain method, by analyzing hash sequences available in each of such packets.Methods. In this article, simulation modeling, system analysis method, method of systematization and ranking of the obtained results are used.Results. It is shown that increasing the size of the additional field with the hash of the previous message from 4 to 6 bits has a significant effect on reducing the probability of erroneous restore of the order of information packets. Further increasing the length of the hash field reduces the probability of error by only 2 to 5 % for each additional bit of the hash field for any length of the chain being restored. It is shown that the coefficient of the usage of the communication channel (the ratio of useful chain of packets to the volume of information transmitted through the communication channel) is maximum when the length of the hash field is 6 in the whole range of sizes of the field information part of the data packet.Conclusion. The paper shows that the chain method is applicable for restoring the original sequence of information packets transmitted from the source to the receiver in systems where the preservation of the sequence of packets is not guaranteed. The obtained values of the transmission system parameters allow us to ensure acceptable reliability of data transmission with a minimum amount of additional service information, and achieve information redundancy less than that in similar ones by 10-15.

Privacy Protection for Medical Images based on DenseNet and Coverless Steganography

With the development of the internet of medical things (IoMT), the privacy protection problem has become more and more critical. In this paper, we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography. For a given group of medical images of one patient, DenseNet is used to regroup the images based on feature similarity comparison. Then the mapping indexes can be constructed based on LBP feature and hash generation. After mapping the privacy information with the hash sequences, the corresponding mapped indexes of secret information will be packed together with the medical images group and released to the authorized user. The user can extract the privacy information successfully with a similar method of feature analysis and index construction. The simulation results show good performance of robustness. And the hiding success rate also shows good feasibility and practicability for application. Since the medical images are kept original without embedding and modification, the performance of crack resistance is outstanding and can keep better quality for diagnosis compared with traditional schemes with data embedding.

A Long Sequence Speech Perceptual Hashing Authentication Algorithm Based on Constant Q Transform and Tensor Decomposition

Most speech authentication algorithms are over-optimized for robustness and efficiency, resulting in poor discrimination. Hashing shorter sequence is likely to cause the same hashing sequence to come from different speech segments, which will cause serious deviations in authentication. Few people pay attention to the research on the discrimination of hashing sequence length, so this paper proposes a long sequence speech authentication algorithm based on constant Q transform (CQT) and tensor decomposition (TD). In this paper, hashing long sequence is used to solve the problem of poor collision resistance of existing algorithms, fast and accurate authentication can be achieved for important speech fragments with large data volumes. The sub-band in the frequency domain are first divided into different matrix, then the variance set of sub-band in the frequency domain is obtained, and finally the feature values are obtained by CQT and TD transformation. The obtained feature values have strong robustness and can cope with the interference of complex channel environment. In this paper, Texas Instruments and Massachusetts Institute of Technology (TIMIT) speech database and the Text to Speech (TTS) are used to establish a database of 51600 speeches to verify the performance of the algorithm. Experimental results show that compared with the existing speech authentication algorithms, the proposed algorithm has the characteristics of high discrimination, strong robustness and high efficiency.