scholarly journals An Image Hashing Algorithm for Authentication with Multi-Attack Reference Generation and Adaptive Thresholding

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 227
Author(s):  
Ling Du ◽  
Zehong He ◽  
Yijing Wang ◽  
Xiaochao Wang ◽  
Anthony T. S. Ho
Keyword(s):  
Similarity Measures ◽  
Image Authentication ◽  
Adaptive Thresholding ◽  
Image Hashing ◽  
Memory Efficiency ◽  
Distorted Version ◽  
Benchmark Datasets ◽  
Information Set ◽  
Threshold Setting ◽  
Hashing Algorithm

Image hashing-based authentication methods have been widely studied with continuous advancements owing to the speed and memory efficiency. However, reference hash generation and threshold setting, which are used for similarity measures between original images and corresponding distorted version, are important but less considered by most of existing models. In this paper, we propose an image hashing method based on multi-attack reference generation and adaptive thresholding for image authentication. We propose to build the prior information set based on the help of multiple virtual prior attacks, and present a multi-attack reference generation method based on hashing clusters. The perceptual hashing algorithm was applied to the reference/queried image to obtain the hashing codes for authentication. Furthermore, we introduce the concept of adaptive thresholding to account for variations in hashing distance. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.

scholarly journals Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Saliency

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Ling Du ◽  
Zhen Chen ◽  
Yongzhen Ke
Keyword(s):  
Semantic Information ◽  
Semantic Content ◽  
Tamper Detection ◽  
Image Hashing ◽  
High Quality ◽  
Multiple Features ◽  
Memory Efficiency ◽  
Benchmark Datasets ◽  
Perceptual Hashing ◽  
Hashing Algorithm

Perceptual hashing technique for tamper detection has been intensively investigated owing to the speed and memory efficiency. Recent researches have shown that leveraging supervised information could lead to learn a high-quality hashing code. However, most existing methods generate hashing code by treating each region equally while ignoring the different perceptual saliency relating to the semantic information. We argue that the integrity for salient objects is more critical and important to be verified, since the semantic content is highly connected to them. In this paper, we propose a Multi-View Semi-supervised Hashing algorithm with Perceptual Saliency (MV-SHPS), which explores supervised information and multiple features into hashing learning simultaneously. Our method calculates the image hashing distance by taking into account the perceptual saliency rather than directly considering the distance value between total images. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.

scholarly journals A Survey on Image Hashing for Image Authentication

2010 ◽  
Vol E93-D (5) ◽  
pp. 1020-1030 ◽  
Author(s):  
Yang OU ◽  
Kyung Hyune RHEE
Keyword(s):  
Image Authentication ◽  
Image Hashing

scholarly journals Adaptive CSLBP compressed image hashing

2019 ◽  
Vol 9 (4) ◽  
pp. 2982
Author(s):  
Varsha Patil ◽  
Tanuja Sarode
Keyword(s):  
Hamming Distance ◽  
Color Space ◽  
Image Authentication ◽  
Image Texture ◽  
Weight Factor ◽  
Image Hashing ◽  
Discrimination Capability ◽  
Content Change ◽  
Smooth Region ◽  
Texture Extraction

Hashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images.

scholarly journals Robust Image Hashing with Low-Rank Representation and Ring Partition

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhenjun Tang ◽  
Zixuan Yu ◽  
Zhixin Li ◽  
Chunqiang Yu ◽  
Xianquan Zhang
Keyword(s):  
Image Quality Assessment ◽  
Characteristic Curve ◽  
Visual Representation ◽  
Image Authentication ◽  
Saliency Map ◽  
Low Rank ◽  
Image Hashing ◽  
Copy Detection ◽  
Social Event Detection ◽  
Low Rank Representation

Image hashing has attracted much attention of the community of multimedia security in the past years. It has been successfully used in social event detection, image authentication, copy detection, image quality assessment, and so on. This paper presents a novel image hashing with low-rank representation (LRR) and ring partition. The proposed hashing finds the saliency map by the spectral residual model and exploits it to construct the visual representation of the preprocessed image. Next, the proposed hashing calculates the low-rank recovery of the visual representation by LRR and extracts the rotation-invariant hash from the low-rank recovery by ring partition. Hash similarity is finally determined by L2 norm. Extensive experiments are done to validate effectiveness of the proposed hashing. The results demonstrate that the proposed hashing can reach a good balance between robustness and discrimination and is superior to some state-of-the-art hashing algorithms in terms of the area under the receiver operating characteristic curve.

Robust Hash for Detecting and Localizing Image Tampering

2010 ◽  
Vol 439-440 ◽  
pp. 1018-1023
Author(s):  
De Long Cui ◽  
Yong Fu Liu ◽  
Jing Long Zuo
Keyword(s):  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Image Hashing ◽  
Post Processing ◽  
Feature Extracting ◽  
Image Tampering ◽  
Secret Keys ◽  
Robust Image ◽  
Hashing Algorithm ◽  
Robust Image Hashing

In order to improve the sensitive to illegal manipulations of image hashing, a novel robust image hashing algorithm based on fractional Fourier transform (FRFT) for detecting and localizing image tampering is proposed in this paper. The framework of generating an image hashing includes three steps: preprocessing, feature extracting and post processing. The robust hashing sequence is obtained by FRFT coefficients of image blocks. The security of proposed algorithm is totally depended on the orders of FRFT which are saved as secret keys. Experimental results show that the proposed scheme is robust against perceptually acceptable modifications to the image such as JPEG compression, mid-filtering, and rotation, more importantly the tampered place can be identified accurately.

Image authentication based on robust image hashing with geometric correction

2018 ◽  
Vol 77 (19) ◽  
pp. 25409-25429 ◽  
Author(s):  
Ram Kumar Karsh ◽  
Arunav Saikia ◽  
Rabul Hussain Laskar
Keyword(s):  
Image Authentication ◽  
Image Hashing ◽  
Geometric Correction ◽  
Robust Image ◽  
Robust Image Hashing
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