Image Hashing Research Articles

Cohesion Intensive Deep Hashing for Remote Sensing Image Retrieval

Recently, the demand for remote sensing image retrieval is growing and attracting the interest of many researchers because of the increasing number of remote sensing images. Hashing, as a method of retrieving images, has been widely applied to remote sensing image retrieval. In order to improve hashing performance, we develop a cohesion intensive deep hashing model for remote sensing image retrieval. The underlying architecture of our deep model is motivated by the state-of-the-art residual net. Residual nets aim at avoiding gradient vanishing and gradient explosion when the net reaches a certain depth. However, different from the residual net which outputs multiple class-labels, we present a residual hash net that is terminated by a Heaviside-like function for binarizing remote sensing images. In this scenario, the representational power of the residual net architecture is exploited to establish an end-to-end deep hashing model. The residual hash net is trained subject to a weighted loss strategy that intensifies the cohesiveness of image hash codes within one class. This effectively addresses the data imbalance problem normally arising in remote sensing image retrieval tasks. Furthermore, we adopted a gradualness optimization method for obtaining optimal model parameters in order to favor accurate binary codes with little quantization error. We conduct comparative experiments on large-scale remote sensing data sets such as UCMerced and AID. The experimental results validate the hypothesis that our method improves the performance of current remote sensing image retrieval.

Secure and Traceable Image Transmission Scheme Based on Semitensor Product Compressed Sensing in Telemedicine System

With the rapid development of the Internet of Things technology and the gradual upgrade of communication methods, a new type of telemedicine system encounters a golden opportunity for development. However, lots of portable sensors for medical health have generated a large amount of sensitive data while bringing convenience to people, which makes the original transmission scheme difficult to meet current transmission requirements. Aiming at realizing high efficiency of image transmission and high security of sensitive image data in the telemedicine system, this article proposes a secure and traceable image transmission scheme. The sensitive image is first compressed and encrypted by the optimized semitensor product compressed sensing algorithm. By the perceptual hash algorithm, the image hash value, also called image fingerprint, is generated from the image content to verify the authenticity of the image. All the image fingerprints are merged to form an image fingerprint chain, which provides the traceability of the image authentication. The digital watermarking technology is used to hide the encrypted sensitive image into a meaningful carrier image, which increases the transmission content without enlarging the transmission load. The experimental results show that the proposed image transmission scheme can effectively improve the transmission efficiency, the reconstruction effect, the security, and the authenticity of the image transmission in the telemedicine systems.

Robust Image Hashing With Singular Values Of Quaternion SVD

Abstract Image hashing is an efficient technique of many multimedia systems, such as image retrieval, image authentication and image copy detection. Classification between robustness and discrimination is one of the most important performances of image hashing. In this paper, we propose a robust image hashing with singular values of quaternion singular value decomposition (QSVD). The key contribution is the innovative use of QSVD, which can extract stable and discriminative image features from CIE L*a*b* color space. In addition, image features of a block are viewed as a point in the Cartesian coordinates and compressed by calculating the Euclidean distance between its point and a reference point. As the Euclidean distance requires smaller storage than the original block features, this technique helps to make a discriminative and compact hash. Experiments with three open image databases are conducted to validate efficiency of our image hashing. The results demonstrate that our image hashing can resist many digital operations and reaches a good discrimination. Receiver operating characteristic curve comparisons illustrate that our image hashing outperforms some state-of-the-art algorithms in classification performance.

Deep Reinforcement Learning for Image Hashing

Deep hashing methods have received much attention recently, which achieve promising results by taking advantage of the strong representation power of deep networks. However, most existing deep hashing methods learn a whole set of hashing functions independently, while ignore the correlations between different hashing functions that can promote the retrieval accuracy greatly. Inspired by the sequential decision ability of deep reinforcement learning, we propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH). Our proposed DRLIH approach models the hashing learning problem as a sequential decision process, which learns each hashing function by correcting the errors imposed by previous ones and promotes retrieval accuracy. To the best of our knowledge, this is the first work to address hashing problem from deep reinforcement learning perspective. The main contributions of our proposed DRLIH approach can be summarized as follows: (1) We propose a deep reinforcement learning hashing network. In the proposed network, we utilize recurrent neural network (RNN) as agents to model the hashing functions, which take actions of projecting images into binary codes sequentially, so that the current hashing function learning can take previous hashing functions' error into account. (2) We propose a sequential learning strategy based on proposed DRLIH. We define the state as a tuple of internal features of RNN's hidden layers and image features, which can reflect history decisions made by the agents. We also propose an action group method to enhance the correlation of hash functions in the same group. Experiments on three widely-used datasets demonstrate the effectiveness of our proposed DRLIH approach.

A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids

Image hash is an alternative to cryptographic hash functions for checking integrity of digital images. Compared to cryptographic hash functions, an image hash or a Perceptual Hash Function (PHF) is resilient to content preserving distortions and sensitive to malicious tampering. In this paper, a robust and secure image hashing technique using a Gaussian pyramid is proposed. A Gaussian pyramid decomposes an image into different resolution levels which can be utilized to obtain robust and compact hash features. These stable features have been utilized in the proposed work to construct a secure and robust image hash. The proposed scheme uses Laplacian of Gaussian (LOG) and disk filters to filter the low-resolution Gaussian decomposed image. The filtered images are then subtracted and their difference is used as a hash. To make the hash secure, a key is introduced before feature extraction, thus making the entire feature space random. The proposed hashing scheme has been evaluated through a number of experiments involving cases of non-malicious distortions and malicious tampering. Experimental results reveal that the proposed hashing scheme is robust against non-malicious distortions and is sensitive to detect minute malicious tampering. Moreover, False Positive Probability (FPP) and False Negative Probability (FNP) results demonstrate the effectiveness of the proposed scheme when compared to state-of-the-art image hashing algorithms proposed in the literature.

Robust Perceptual Image Hashing using SIFT and SVD

With the advancement in technology, digital data such as image, video, etc. can be easily manipulated. Image hashing is a method that can be used for authentication and identification of digital images. In this communication, a robust image hashing technique is proposed using scale invariant feature transform (SIFT), singular value decomposition (SVD) and Zernike moment. Zernike moment is used to restore the image against the rotation attack. Potential points are generated from the image by using SIFT. Block processing of equal size is performed on the input image. Key points within the same block are used to generate hashing values. The experimental outcome shows that the proposed technique can withstand different types of attacks. Receiver operating characteristic curve comparison specifies that our method outperforms other existing methods under consideration.

WeGAN: Deep Image Hashing With Weighted Generative Adversarial Networks

Image hashing has been widely used in image retrieval tasks. Many existing methods generate hashing codes based on image feature representations. They rarely consider the rich information such as image clustering information contained in the image set as well as uncertain relationships between images and tags simultaneously. In this paper, we develop a Weighted Generative Adversarial Networks (WeGAN) to transfer the clustering information of images to construct the hashing code. WeGAN consists three modules: 1) a hashing learning process for transferring knowledge of the image set to hashing codes of single images; 2) by means of hashing codes, a module to generate image content, tag representation, and their joint information which reflects the correlation between the image and the corresponding tags; 3) a discriminator to distinguish the generated data from the original source, and then formulating three loss functions. Different weights are assigned to these loss functions in order to deal with the uncertainties between images and tags. Through introducing the image set to process the image hashing with different tags, WeGAN can naturally provide the information of clustering results, which is useful for image hashing with multi-tags. The generated hashing code has the ability to dynamically process the uncertain relationships between images and tags. Experiments on three challenging datasets show that WeGAN outperforms the state-of-the-art methods.

Image encryption algorithm based on image hashing, improved chaotic mapping and DNA coding

In order to improve the problems of security and robustness for existing image encryption algorithms and to reduce the security risks of encryption algorithms against statistical analysis, differential attacks, exhaustive attacks, cropping and noise attacks etc., a novel image encryption algorithm based on image hashing, improved chaotic mapping and DNA coding is proposed. Firstly, extracting the image features and evenly block after pre-processing the original image and fingerprint image. Secondly, the features are generated to be a binary hash sequence through the image hash algorithm, and the generated hash sequence is iterated as the initial parameter of the improved chaotic map and Chen's chaotic system. Finally, Chen's chaotic system is used to generate a random sequence, and the matrix generated by the improved chaotic map and the original image is subjected to DNA calculation and encoding operations to obtain an encrypted image. Experimental results show that the proposed algorithm has better performance on security, larger key space and higher key sensitivity, the pixel correlation coefficient close to 0, the information entropy close to 8, unified average changing intensity and number of pixels change rate values are close to ideal values, and it has better robustness on noise and cropping attacks.

Triplet Deep Hashing with Joint Supervised Loss Based on Deep Neural Networks.

In recent years, with the explosion of multimedia data from search engines, social media, and e-commerce platforms, there is an urgent need for fast retrieval methods for massive big data. Hashing is widely used in large-scale and high-dimensional data search because of its low storage cost and fast query speed. Thanks to the great success of deep learning in many fields, the deep learning method has been introduced into hashing retrieval, and it uses a deep neural network to learn image features and hash codes simultaneously. Compared with the traditional hashing methods, it has better performance. However, existing deep hashing methods have some limitations; for example, most methods consider only one kind of supervised loss, which leads to insufficient utilization of supervised information. To address this issue, we proposed a triplet deep hashing method with joint supervised loss based on the convolutional neural network (JLTDH) in this work. The proposed method JLTDH combines triplet likelihood loss and linear classification loss; moreover, the triplet supervised label is adopted, which contains richer supervised information than that of the pointwise and pairwise labels. At the same time, in order to overcome the cubic increase in the number of triplets and make triplet training more effective, we adopt a novel triplet selection method. The whole process is divided into two stages: In the first stage, taking the triplets generated by the triplet selection method as the input of the CNN, the three CNNs with shared weights are used for image feature learning, and the last layer of the network outputs a preliminary hash code. In the second stage, relying on the hash code of the first stage and the joint loss function, the neural network model is further optimized so that the generated hash code has higher query precision. We perform extensive experiments on the three public benchmark datasets CIFAR-10, NUS-WIDE, and MS-COCO. Experimental results demonstrate that the proposed method outperforms the compared methods, and the method is also superior to all previous deep hashing methods based on the triplet label.

Bidirectional Discrete Matrix Factorization Hashing for Image Search.

Unsupervised image hashing has recently gained significant momentum due to the scarcity of reliable supervision knowledge, such as class labels and pairwise relationship. Previous unsupervised methods heavily rely on constructing sufficiently large affinity matrix for exploring the geometric structure of data. Nevertheless, due to lack of adequately preserving the intrinsic information of original visual data, satisfactory performance can hardly be achieved. In this article, we propose a novel approach, called bidirectional discrete matrix factorization hashing (BDMFH), which alternates two mutually promoted processes of 1) learning binary codes from data and 2) recovering data from the binary codes. In particular, we design the inverse factorization model, which enforces the learned binary codes inheriting intrinsic structure from the original visual data. Moreover, we develop an efficient discrete optimization algorithm for the proposed BDMFH. Comprehensive experimental results on three large-scale benchmark datasets show that the proposed BDMFH not only significantly outperforms the state-of-the-arts but also provides the satisfactory computational efficiency.

Image alignment based perceptual image hash for content authentication

Perceptual image hash is an emerging technology that is closely related to many applications such as image content authentication, image forging detection, image similarity detection, and image retrieval. In this work, we propose an image alignment based perceptual image hash method, and a hash-based image forging detection and tampering localization method. In the proposed method, we introduce an image alignment process to provide a framework for image hash method to tolerate a wide range of geometric distortions. The image hash is generated by utilizing hybrid perceptual features that are extracted from global and local Zernike moments combining with DCT-based statistical features of the image. The proposed method can detect various image forging and compromised image regions. Furthermore, it has broad-spectrum robustness, including tolerating content-preserving manipulations and geometric distortion-resilient. Compared with state-of-the-art schemes, the proposed method provides satisfactory comprehensive performances in content-based image forging detection and tampering localization.

Scalable Deep Hashing for Large-scale Social Image Retrieval.

Recent years have witnessed the wide application of hashing for large-scale image retrieval, because of its high computation efficiency and low storage cost. Particularly, benefiting from current advances in deep learning, supervised deep hashing methods have greatly boosted the retrieval performance, under the strong supervision of large amounts of manually annotated semantic labels. However, their performance is highly dependent upon the supervised labels, which significantly limits the scalability. In contrast, unsupervised deep hashing without label dependence enjoys the advantages of well scalability. Nevertheless, due to the relaxed hash optimization, and more importantly, the lack of semantic guidance, existing methods suffer from limited retrieval performance. In this paper, we propose a SCAlable Deep Hashing (SCADH) to learn enhanced hash codes for social image retrieval. We formulate a unified scalable deep hash learning framework which explores the weak but free supervision of discriminative user tags that are commonly accompanied with social images. It jointly learns image representations and hash functions with deep neural networks, and simultaneously enhances the discriminative capability of image hash codes with the refined semantics from the accompanied social tags. Further, instead of simple relaxed hash optimization, we propose a discrete hash optimization method based on Augmented Lagrangian Multiplier to directly solve the hash codes and avoid the binary quantization information loss. Experiments on two standard social image datasets demonstrate the superiority of the proposed approach compared with stateof- the-art shallow and deep hashing techniques.

Coarse-to-Fine Image DeHashing Using Deep Pyramidal Residual Learning

Image dehashing refers to the process of inferring images by inverting image hashes. Recently, image dehashing from real-valued image retrieval hashes is shown feasible using deep convolutional neural networks. However, the perceptual quality of dehashed images is challenged when real-valued hashes are quantized to less bits. Besides, the scalability to larger or color image dehashing is limited in the previous dehashing network. To this end, we propose a pyramidal long-range residual-learning network (PyLRR-Net). PyLRR-Net is a pyramidal image reconstruction network to dehash images in a progressive manner. At each image scale, we design and insert a long-range residual block to refine the coarse image reconstruction leveraging deep residual learning. Experiments on both grayscale and color image datasets show that the proposed PyLRR-Net outperforms previous work in terms of image dehashing quality, scalability, and flexibility for large and color image dehashing problems.

Perceptual hashing for color image based on color opponent component and quadtree structure

In this paper, a perceptual image hashing scheme based on color opponent component (COC) and Quadtree structure is proposed. Firstly, image normalization and Gaussian low-pass filtering are applied on input image to produce a secondary image, and then COC is extracted from the secondary image. The color change information is calculated from COC as color feature and Quadtree decomposition (QD) is applied on the intensity image of secondary image to produce Quadtree structure features. Finally, color features and Quadtree structure features are concatenated and pseudo-randomly permuted to produce final hash sequence. Experimental results show that the proposed scheme is robust to common content-preserving manipulations. The receiver operating characteristics (ROC) curves show that the proposed method outperforms some state-of-the-art methods in the performances of perceptual robustness and discrimination.

A PUF-Based Data-Device Hash for Tampered Image Detection and Source Camera Identification

With the increasing prevalent of digital devices and their abuse for digital content creation, forgeries of digital images and video footage are more rampant than ever. Digital forensics is challenged into seeking advanced technologies for forgery content detection and acquisition device identification. Unfortunately, existing solutions that address image tampering problems fail to identify the device that produces the images or footage while techniques that can identify the camera is incapable of locating the tampered content of its captured images. In this paper, a new perceptual data-device hash is proposed to locate maliciously tampered image regions and identify the source camera of the received image data as a non-repudiable attestation in digital forensics. The presented image may have been either tampered or gone through benign content preserving geometric transforms or image processing operations. The proposed image hash is generated by projecting the invariant image features into a physical unclonable function (PUF)-defined Bernoulli random space. The tamper-resistant random PUF response is unique for each camera and can only be generated upon triggered by a challenge, which is provided by the image acquisition timestamp. The proposed hash is evaluated on the modified CASIA database and CMOS image sensor-based PUF simulated using 180 nm TSMC technology. It achieves a high tamper detection rate of 95.42% with the regions of tampered content successfully located, a good authentication performance of above 98.5% against standard content-preserving manipulations, and 96.25% and 90.42%, respectively, for the more challenging geometric transformations of rotation (0 ~ 360°) and scaling (scale factor in each dimension: 0.5). It is demonstrated to be able to identify the source camera with 100% accuracy and is secure against attacks on PUF.

A Review of Methods to Quantify the Genomic Similarity of Topological Associating Domains.

Topologically associating domains (TADs) are the most fundamental elements and significant structures of the eukaryotic genome. Currently, algorithms have been developed to find the TADs. But few algorithms are reported to compare the similarity of TADs between genomes. In this study, mice Hi-C sequencing data of four contrasts were enrolled. Seventeen algorithms, including BPscore, Jaccard index (JI) distance, VI distance, image hash, image subtraction, image variance, and so on, were used to quantify the genomic similarity of TADs. Image subtraction, Euclidean distance, and Manhattan distance were significantly better for TAD difference detection than the others. Deferent Hash (dHash) with the best zoom size ranked the second, followed by improved Hamming distance algorithm and JI distance. Advantages and disadvantages of various algorithms for quantifying the similarity of TADs were compared. Our work could provide the fundament for TADs comparison.

Large-Scale Person Re-Identification Based on Deep Hash Learning.

Person re-identification in the image processing domain has been a challenging research topic due to the influence of pedestrian posture, background, lighting, and other factors. In this paper, the method of harsh learning is applied in person re-identification, and we propose a person re-identification method based on deep hash learning. By improving the conventional method, the method proposed in this paper uses an easy-to-optimize shallow convolutional neural network to learn the inherent implicit relationship of the image and then extracts the deep features of the image. Then, a hash layer with three-step calculation is incorporated in the fully connected layer of the network. The hash function is learned and mapped into a hash code through the connection between the network layers. The generation of the hash code satisfies the requirements that minimize the error of the sum of quantization loss and Softmax regression cross-entropy loss, which achieve the end-to-end generation of hash code in the network. After obtaining the hash code through the network, the distance between the pedestrian image hash code to be retrieved and the pedestrian image hash code library is calculated to implement the person re-identification. Experiments conducted on multiple standard datasets show that our deep hashing network achieves the comparable performances and outperforms other hashing methods with large margins on Rank-1 and mAP value identification rates in pedestrian re-identification. Besides, our method is predominant in the efficiency of training and retrieval in contrast to other pedestrian re-identification algorithms.

Compressed Image Hashing using Minimum Magnitude CSLBP

Image hashing allows compression, enhancement or other signal processing operations on digital images which are usually acceptable manipulations. Whereas, cryptographic hash functions are very sensitive to even single bit changes in image. Image hashing is a sum of important quality features in quantized form. In this paper, we proposed a novel image hashing algorithm for authentication which is more robust against various kind of attacks. In proposed approach, a short hash code is obtained by using minimum magnitude Center Symmetric Local Binary Pattern (CSLBP). The desirable discrimination power of image hash is maintained by modified Local Binary Pattern(LBP) based edge weight factor generated from gradient image. The proposed hashing method extracts texture features using the Center Symmetric Local Binary Pattern (CSLBP). The discrimination power of hashing is increased by weight factor during CSLBP histogram construction. The generated histogram is compressed to 1/4 of the original histogram by minimum magnitude CSLBP. The proposed method, has a twofold advantage, first is a small length and second is acceptable discrimination power. Experimental results are demonstrated by hamming distance, TPR, FPR and ROC curves. Therefore the proposed method successfully does a fair classification of content preserving and content changing images.

SSL: A Novel Image Hashing Technique Using SIFT Keypoints with Saliency Detection and LBP Feature Extraction against Combinatorial Manipulations

Image hashing schemes have been widely used in content authentication, image retrieval, and digital forensic. In this paper, a novel image hashing algorithm (SSL) by incorporating the most stable keypoints and local region features is proposed, which is robust against various content-preserving manipulations, even multiple combinatorial manipulations. The proposed algorithm combines S_cale invariant feature transform (SIFT) with S_aliency detection to extract the most stable keypoints. Then, the L_ocal binary pattern (LBP) feature extraction method is exploited to generate local region features based on these keypoints. After that, the information of keypoints and local region features are merged into a hash vector. Finally, a secret key is used to randomize the hash vector, which can prevent attackers from forging the image and the hash value. Experimental results demonstrate that the proposed hashing algorithm can identify visually similar images which are under both single and combinatorial content-preserving manipulations, even multiple combinations of manipulations. It can also identify maliciously forged images which are under various content-changing manipulations. The collision probability between hashes of different images is nearly zero. Besides, the evaluation of key-dependent security shows that the proposed scheme is secure that an attacker cannot forge or estimate the correct hash value without the knowledge of the secret key.

Robust Image Hashing with Tensor Decomposition

This paper presents a new image hashing that is designed with tensor decomposition (TD), referred to as TD hashing, where image hash generation is viewed as deriving a compact representation from a tensor. Specifically, a stable three-order tensor is first constructed from the normalized image, so as to enhance the robustness of our TD hashing. A popular TD algorithm, called Tucker decomposition, is then exploited to decompose the three-order tensor into a core tensor and three orthogonal factor matrices. As the factor matrices can reflect intrinsic structure of original tensor, hash construction with the factor matrices makes a desirable discrimination of the TD hashing. To examine these claims, there are 14,551 images selected for our experiments. A receiver operating characteristics (ROC) graph is used to conduct theoretical analysis and the ROC comparisons illustrate that the TD hashing outperforms some state-of-the-art algorithms in classification performance between the robustness and discrimination.