scholarly journals Searchable Encrypted Image Retrieval Based on Multi-Feature Adaptive Late-Fusion

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1019
Author(s):  
Wentao Ma ◽  
Jiaohua Qin ◽  
Xuyu Xiang ◽  
Yun Tan ◽  
Zhibin He
Keyword(s):  
Image Retrieval ◽  
Area Under The Curve ◽  
Similarity Score ◽  
Semantic Features ◽  
Cloud Environment ◽  
Late Fusion ◽  
Color Channel ◽  
Encrypted Image ◽  
Single Feature ◽  
Encrypted Images

Recently, searchable encrypted image retrieval in a cloud environment has been widely studied. However, the inappropriate encryption mechanism and single feature description make it hard to achieve the expected effects. Therefore, a major challenge of encrypted image retrieval is how to extract and fuse multiple efficient features to improve performance. Towards this end, this paper proposes a searchable encrypted image retrieval based on multi-feature adaptive late-fusion in a cloud environment. Firstly, the image encryption is completed by designing the encryption function in an RGB color channel, bit plane and pixel position of the image. Secondly, the encrypted images are uploaded to the cloud server and the convolutional neural network (CNN) is fine-tuned to build a semantic feature extractor. Then, low-level features and semantic features are extracted. Finally, the similarity score curves of each feature are calculated, and adaptive late-fusion is performed by the area under the curve. A large number of experiments on public dateset are used to validate the effectiveness of our method.

scholarly journals Toward Efficient Encrypted Image Retrieval in Cloud Environment

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 174541-174550 ◽  
Author(s):  
Zhengbai Huang ◽  
Meng Zhang ◽  
Yi Zhang
Keyword(s):  
Image Retrieval ◽  
Cloud Environment ◽  
Encrypted Image

scholarly journals Improved CNN-Based Hashing for Encrypted Image Retrieval

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wenyan Pan ◽  
Meimin Wang ◽  
Jiaohua Qin ◽  
Zhili Zhou
Keyword(s):  
Image Retrieval ◽  
Computational Cost ◽  
Image Data ◽  
Image Size ◽  
Encrypted Image ◽  
Compact Binary ◽  
Hash Code ◽  
Retrieval Efficiency ◽  
Efficient Retrieval ◽  
Encrypted Images

As more and more image data are stored in the encrypted form in the cloud computing environment, it has become an urgent problem that how to efficiently retrieve images on the encryption domain. Recently, Convolutional Neural Network (CNN) features have achieved promising performance in the field of image retrieval, but the high dimension of CNN features will cause low retrieval efficiency. Also, it is not suitable to directly apply them for image retrieval on the encryption domain. To solve the above issues, this paper proposes an improved CNN-based hashing method for encrypted image retrieval. First, the image size is increased and inputted into the CNN to improve the representation ability. Then, a lightweight module is introduced to replace a part of modules in the CNN to reduce the parameters and computational cost. Finally, a hash layer is added to generate a compact binary hash code. In the retrieval process, the hash code is used for encrypted image retrieval, which greatly improves the retrieval efficiency. The experimental results show that the scheme allows an effective and efficient retrieval of encrypted images.

scholarly journals Deep Hash with Improved Dual Attention for Image Retrieval

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 285
Author(s):  
Wenjing Yang ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li ◽  
Anyu Du
Keyword(s):  
Image Retrieval ◽  
Contextual Information ◽  
Quantization Error ◽  
Feature Representation ◽  
Semantic Features ◽  
Average Value ◽  
Hash Code ◽  
Feature Information ◽  
Learning To Hash ◽  
Study Designs

Recently, deep learning to hash has extensively been applied to image retrieval, due to its low storage cost and fast query speed. However, there is a defect of insufficiency and imbalance when existing hashing methods utilize the convolutional neural network (CNN) to extract image semantic features and the extracted features do not include contextual information and lack relevance among features. Furthermore, the process of the relaxation hash code can lead to an inevitable quantization error. In order to solve these problems, this paper proposes deep hash with improved dual attention for image retrieval (DHIDA), which chiefly has the following contents: (1) this paper introduces the improved dual attention mechanism (IDA) based on the ResNet18 pre-trained module to extract the feature information of the image, which consists of the position attention module and the channel attention module; (2) when calculating the spatial attention matrix and channel attention matrix, the average value and maximum value of the column of the feature map matrix are integrated in order to promote the feature representation ability and fully leverage the features of each position; and (3) to reduce quantization error, this study designs a new piecewise function to directly guide the discrete binary code. Experiments on CIFAR-10, NUS-WIDE and ImageNet-100 show that the DHIDA algorithm achieves better performance.

scholarly journals High-Capacity Reversible Data Hiding in Encrypted Images by Information Preprocessing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xi-Yan Li ◽  
Xia-Bing Zhou ◽  
Qing-Lei Zhou ◽  
Shi-Jing Han ◽  
Zheng Liu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
Main Idea ◽  
Original Image ◽  
Chaos Encryption ◽  
Secret Information ◽  
Encrypted Image ◽  
Encrypted Images ◽  
Scanned Documents

With the development of cloud computing, high-capacity reversible data hiding in an encrypted image (RDHEI) has attracted increasing attention. The main idea of RDHEI is that an image owner encrypts a cover image, and then a data hider embeds secret information in the encrypted image. With the information hiding key, a receiver can extract the embedded data from the hidden image; with the encryption key, the receiver reconstructs the original image. In this paper, we can embed data in the form of random bits or scanned documents. The proposed method takes full advantage of the spatial correlation in the original images to vacate the room for embedding information before image encryption. By jointly using Sudoku and Arnold chaos encryption, the encrypted images retain the vacated room. Before the data hiding phase, the secret information is preprocessed by a halftone, quadtree, and S-BOX transformation. The experimental results prove that the proposed method not only realizes high-capacity reversible data hiding in encrypted images but also reconstructs the original image completely.

scholarly journals Two-level hiding an encrypted image

2020 ◽  
Vol 18 (2) ◽  
pp. 961
Author(s):  
Faten H. MohammedSediq Al-Kadei
Keyword(s):  
Research Area ◽  
Cover Image ◽  
Perceptual Quality ◽  
Fingerprint Image ◽  
Bank Account ◽  
Secret Image ◽  
Encrypted Image ◽  
Encrypted Images ◽  
In The Beginning ◽  
Rgb Image

<p><span>Encryption and hiding images are becoming a hot research area and a broad prospect for application. This article uses a secure algorithm with Low Significant Bit method to hide an encrypted high-resolution color bitmap image in two selectively color images (i.e., two cover images). The paper introduces a two-level hiding encrypted image using MATLAB-GUI programming language. In the beginning, with a key image using XOR bit operation, the original RGB image is encrypted. After that, the encrypted image is hidden into the first cover image. The cover image is then hidden into another cover to make the secret image safer without changing the perceptual quality for both covers. Then, the algorithm is tested on many bitmap images, which can be an important image, fingerprint image, samples of secret medicine, or bank account pattern. The correlation histograms demonstrate a high correlation for all encrypted images. The PSNR is used to find steganography quality for the two cover images after hiding the secret image showing a high quality for the two levels of hiding operation</span></p>

scholarly journals Color Based Image Retrieval by Combining Various Features

2019 ◽  
Vol 9 (2) ◽  
pp. 454-460
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Visual Features ◽  
Semantic Features ◽  
Retrieval Rate ◽  
Image Retrieval System ◽  
Mathematical Formulas ◽  
Image Set ◽  
Different Color ◽  
Image Collection

Content based image retrieval system retrieve the images according to the strong feature related to desire as color, texture and shape of an image. Although visual features cannot be completely determined by semantic features, but still semantic features can be integrate easily into mathematical formulas. This paper is focused on retrieval of images within a large image collection, based on color projection by applying segmentation and quantification on different color models and compared for good result. This method is applied on different categories of image set and evaluated its retrieval rate in different models

scholarly journals Implementation of early and late fusion methods for content-based image retrieval

2021 ◽  
Vol 8 (7) ◽  
pp. 97-105
Author(s):  
Ali Ahmed ◽  
◽  
Sara Mohamed ◽  
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Image Features ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Late Fusion ◽  
Query Image ◽  
Fusion Methods ◽  
And Performance ◽  
Image Repository

Content-Based Image Retrieval (CBIR) systems retrieve images from the image repository or database in which they are visually similar to the query image. CBIR plays an important role in various fields such as medical diagnosis, crime prevention, web-based searching, and architecture. CBIR consists mainly of two stages: The first is the extraction of features and the second is the matching of similarities. There are several ways to improve the efficiency and performance of CBIR, such as segmentation, relevance feedback, expansion of queries, and fusion-based methods. The literature has suggested several methods for combining and fusing various image descriptors. In general, fusion strategies are typically divided into two groups, namely early and late fusion strategies. Early fusion is the combination of image features from more than one descriptor into a single vector before the similarity computation, while late fusion refers either to the combination of outputs produced by various retrieval systems or to the combination of different rankings of similarity. In this study, a group of color and texture features is proposed to be used for both methods of fusion strategies. Firstly, an early combination of eighteen color features and twelve texture features are combined into a single vector representation and secondly, the late fusion of three of the most common distance measures are used in the late fusion stage. Our experimental results on two common image datasets show that our proposed method has good performance retrieval results compared to the traditional way of using single features descriptor and also has an acceptable retrieval performance compared to some of the state-of-the-art methods. The overall accuracy of our proposed method is 60.6% and 39.07% for Corel-1K and GHIM-10K ‎datasets, respectively.

Sign in / Sign up

Export Citation Format

Share Document