Proceedings of the IEEE International Conference on Image Processing

This paper introduces an image encryption algorithm shorted as CITSPD, manipulated by circle index table scrambling and partition diffusion Firstly, the circle index table is obtained through the generation, circle shift and transposition of the benchmark sequence Secondly, the plain image is transformed into the wavelet coefficient and is then scrambled by the circle index table Thirdly, the permutated image is disturbed by different noises and is further divided into four subsections Finally, the forward and inverse partition diffusions are performed to the subsections for getting the cipher image The main feature of this algorithm is that the robust chaos-based keystream and encryption process are highly sensitive to the plaintext, which will effectively resist against chosen-plaintext and known-plaintext attacks In addition, the encryption scheme is free of noise attack since the inverse diffusion differs from the forward one And the diffusion effect can be effectively enhanced by, as much as possible, increasing the small pixel value and decreasing the large pixel value Experimental tests and security analyses are carried out to verify the advantages of the scheme

Image encryption algorithm with circle index table scrambling and partition diffusion

In this paper, a novel perceptual image hashing scheme based on convolutional neural network (CNN) with multiple constraints is proposed, in which our deep hashing network learns the process of features extraction automatically according to the training target and then generates the final hash sequence. The combination of convolutional and pooling layers is to reduce the size of input image while deepening the channels. Then, we construct two pairs of constraints and integrate them into an overall constraint function through a strategy of weight allocation. In order to guarantee the robustness and discrimination of deep hashing network simultaneously, a new training method is developed to adjust the training set structure dynamically according to the changes of constraint values. Experimental results show that the proposed deep hashing network can achieve a satisfactory balance between perceptual robustnzess and discrimination while maintaining security. Based on the large-scale test set, receiver operating characteristic (ROC) curves, ${F}_{1}$ scores and equal error rate (EER) demonstrate the superiority of our scheme in terms of content authentication compared with some state-of-the-art schemes.

Perceptual Image Hashing for Content Authentication Based on Convolutional Neural Network With Multiple Constraints

Image hashing is a useful technology of many multimedia systems, such as image retrieval, image copy detection, multimedia forensics and image authentication. Most of the existing hashing algorithms do not reach a good classification between robustness and discrimination and some hashing algorithms based on dimensionality reduction have high computational cost. To solve these problems, we propose a robust and fast image hashing based on two-dimensional (2D) principal component analysis (PCA) and saliency map. The saliency map determined by a visual attention model called LC (luminance contrast) method can ensure good robustness of our hashing. Since 2D PCA is a fast and efficient technique of dimensionality reduction, the use of 2D PCA helps to learn a compact and discriminative code and provide a fast speed of our hashing. Extensive experiments are carried out to validate the performances of our hashing. Classification comparison shows that our hashing is better than some state-of-the-art algorithms. Computational time comparison illustrates that our hashing outperforms some compared algorithms based on dimensionality reduction.

Robust and fast image hashing with two-dimensional PCA

In the recent digitization era, image hashing is a key technology, including image recognition, authentication and manipulation detection, among many multimedia security applications. The primary challenge in hashing schemes is to extract its robust feature. For a better understanding and design of a robust image hashing algorithm, it is very crucial to look into few important parameters like discrimination, robustness, reliability, etc. This paper reflects a detailed study of the existing literature on hashing-based image authentication techniques. This work provides a systematic overview and highlights the merits and demerits associated with various state-of-the-art techniques. In particular, the basic features and categories of image authentication techniques based on hashing are explored along with their properties. Moreover, different performance measures such as output metrices, receiver operating characteristics (ROC) parameters, execution time, etc., have been discussed in this work. The paper also compares the performances of various existing algorithms related to different content preserving operations on diverse data sets. This paper summarizes all the techniques and provides the most optimum solutions in regard to image hashing techniques based on different parameters.

A review of hashing based image authentication techniques

\n 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.

Robust Image Hashing With Singular Values Of Quaternion SVD

Image hashing is an efficient technique of multimedia processing for many applications, such as image copy detection, image authentication, and social event detection. In this study, the authors propose a novel image hashing with visual attention model and invariant moments. An important contribution is the weighted DWT (discrete wavelet transform) representation by incorporating a visual attention model called Itti saliency model into LL sub-band. Since the Itti saliency model can efficiently extract saliency map reflecting regions of attention focus, perceptual robustness of the proposed hashing is achieved. In addition, as invariant moments are robust and discriminative features, hash construction with invariant moments extracted from the weighted DWT representation ensures good classification performance between robustness and discrimination. Extensive experiments with open image datasets are done to validate the performances of the proposed hashing. The results demonstrate that the proposed hashing is robust and discriminative. Performance comparisons with some hashing algorithms are also conducted, and the receiver operating characteristic results illustrate that the proposed hashing outperforms the compared hashing algorithms in classification performance between robustness and discrimination.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2019.1157

Robust image hashing with visual attention model and invariant moments

Image hashing is an efficient technology for processing digital images and has been successfully used in image copy detection, image retrieval, image authentication, image quality assessment, and so on. In this paper, we design a new image hashing with compressed sensing (CS) and ordinal measures. This hashing method uses a visual attention model called Itti model and Canny operator to construct an image representation, and exploits CS to extract compact features from the representation. Finally, the CS-based compact features are quantized via ordinal measures. L2 norm is used to judge similarity of hashes produced by the proposed hashing method. Experiments about robustness validation, discrimination test, block size discussion, selection of visual attention model, selection of quantization scheme, and effectiveness of the use of ordinal measures are conducted to verify performances of the proposed hashing method. Comparisons with some state-of-the-art algorithms are also carried out. The results illustrate that the proposed hashing method outperforms some compared algorithms in classification according to ROC (receiver operating characteristic) graph.

/pdf/robust-image-hashing-with-compressed-sensing-and-ordinal-5a21gsh0fo.pdf

Hanyun Zhang

Papers

Robust Image Hashing With Singular Values Of Quaternion SVD

Robust image hashing with visual attention model and invariant moments

Robust image hashing with compressed sensing and ordinal measures