scholarly journals A Perceptual Image Hashing Algorithm for Hybrid Document Security

2017 ◽  
Author(s):  
Sebastien Eskenazi ◽  
Boris Bodin ◽  
Petra Gomez-Kramer ◽  
Jean-Marc Ogier
Keyword(s):  
Image Hashing ◽  
Document Security ◽  
Hashing Algorithm

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.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Mingfu Xue ◽  
Chengxiang Yuan ◽  
Zhe Liu ◽  
Jian Wang
Keyword(s):  
Feature Extraction ◽  
Saliency Detection ◽  
Local Region ◽  
Collision Probability ◽  
Image Hashing ◽  
Content Authentication ◽  
Secret Key ◽  
Feature Extraction Method ◽  
Sift Keypoints ◽  
Hashing Algorithm

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.

Perceptual image hashing algorithm-based on difference quantization of histogram and chaos system

2009 ◽  
Vol 28 (11) ◽  
pp. 2804-2807
Author(s):  
Shao-jiang DENG ◽  
Fang-xiao WANG ◽  
Dai-gu ZHANG ◽  
Yu WANG
Keyword(s):  
Image Hashing ◽  
Chaos System ◽  
Hashing Algorithm

scholarly journals Image Hashing Algorithm Based on Mirror Flipping and a Three-Dimensional Space Angle

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yan Zhao ◽  
Shuai Liu
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Image Features ◽  
Morphological Features ◽  
Image Hashing ◽  
Space Angle ◽  
Opposite Color ◽  
Hashing Algorithm ◽  
Color Components ◽  
Three Dimensional Space

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.

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