Robust Image Hash Function Based on Polar Harmonic Transforms and Feature Selection

2012 ◽  
Author(s):  
Y.N. Li
Keyword(s):  
Feature Selection ◽  
Hash Function ◽  
Image Hash ◽  
Robust Image

Robust image hash function using local color features

2013 ◽  
Vol 67 (8) ◽  
pp. 717-722 ◽  
Author(s):  
Zhenjun Tang ◽  
Xianquan Zhang ◽  
Xuan Dai ◽  
Jianzhong Yang ◽  
Tianxiu Wu
Keyword(s):  
Hash Function ◽  
Local Color ◽  
Color Features ◽  
Image Hash ◽  
Robust Image

scholarly journals Large-Scale Person Re-Identification Based on Deep Hash Learning

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 449 ◽  
Author(s):  
Xian-Qin Ma ◽  
Chong-Chong Yu ◽  
Xiu-Xin Chen ◽  
Lan Zhou
Keyword(s):  
Hash Function ◽  
Large Scale ◽  
Research Topic ◽  
Cross Entropy ◽  
Network Layers ◽  
Hash Code ◽  
Image Hash ◽  
Challenging Research ◽  
Fully Connected ◽  
Relationship Of

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.

scholarly journals A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1132 ◽  
Author(s):  
Iram Bashir ◽  
Fawad Ahmed ◽  
Jawad Ahmad ◽  
Wadii Boulila ◽  
Nouf Alharbi
Keyword(s):  
False Negative ◽  
Hash Functions ◽  
Feature Space ◽  
Image Hashing ◽  
Gaussian Pyramid ◽  
Image Hash ◽  
Perceptual Hash ◽  
Robust Image ◽  
Robust Image Hashing ◽  
Cryptographic Hash Functions

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.

Perceptual image hash function via associative memory‐based self‐correcting

2018 ◽  
Vol 54 (4) ◽  
pp. 208-210 ◽  
Author(s):  
Yuenan Li ◽  
Dongdong Wang ◽  
Jingru Wang
Keyword(s):  
Associative Memory ◽  
Hash Function ◽  
Image Hash

A Robust Image Perceptual Hashing Scheme Based on Improved Normalization Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1389-1393
Author(s):  
Shu Sen Sun ◽  
Yong Zeng ◽  
Hua Xiong Zhang ◽  
Jiang Sheng Gui
Keyword(s):  
Discrete Cosine Transform ◽  
Normalization Method ◽  
Image Feature ◽  
Original Image ◽  
Affine Transformations ◽  
Image Normalization ◽  
Image Hash ◽  
Dct Coefficients ◽  
Perceptual Hashing ◽  
Robust Image

An improved image normalization algorithm is proposed firstly. Then a geometric robust image perceptual hashing scheme is proposed based on image normalization and discrete cosine transform. The original image is preprocessed using our improved image normalization method. And the selected DCT coefficients as image feature are encrypted for security. The geometric robust image hash is achieved by quantizing encrypted DCT coefficients and coding. The experimental results show that the algorithm can resist against common global affine transformations such as rotation, scaling, translation and their combinations.

Robust image hash in Radon transform domain for authentication

2011 ◽  
Vol 26 (6) ◽  
pp. 280-288 ◽  
Author(s):  
Yanqiang Lei ◽  
Yuangen Wang ◽  
Jiwu Huang
Keyword(s):  
Radon Transform ◽  
Transform Domain ◽  
Image Hash ◽  
Robust Image
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