IEEE Transactions on Pattern Analysis and Machine Intelligence

On the fault-tolerant performance for a class of robust image steganography

Color medical image lossless watermarking using chaotic system and accurate quaternion polar harmonic transforms

In image processing, the rapid approximate solution of variational problems involving generic data-fitting terms is often of practical relevance, for example in real-time applications. Variational solvers based on diffusion schemes or the Euler-Lagrange equations are too slow and restricted in the types of data-fitting terms. Here, we present a filter-based approach to reduce variational energies that contain generic data-fitting terms, but are restricted to specific regularizations. Our approach is based on reducing the regularization part of the variational energy, while guaranteeing non-increasing total energy. This is applicable to regularization-dominated models, where the data-fitting energy initially increases, while the regularization energy initially decreases. We present fast discrete filters for regularizers based on Gaussian curvature, mean curvature, and total variation. These pixel-local filters can be used to rapidly reduce the energy of the full model. We prove the convergence of the resulting iterative scheme in a greedy sense, and we show several experiments to demonstrate applications in image-processing problems involving regularization-dominated variational models.

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Curvature Filters Efficiently Reduce Certain Variational Energies

Due to the rapid growth of machine learning tools and specifically deep networks in various computer vision and image processing areas, applications of Convolutional Neural Networks for watermarking have recently emerged. In this paper, we propose a deep end-to-end diffusion watermarking framework (ReDMark) which can learn a new watermarking algorithm in any desired transform space. The framework is composed of two Fully Convolutional Neural Networks with residual structure which handle embedding and extraction operations in real-time. The whole deep network is trained end-to-end to conduct a blind secure watermarking. The proposed framework simulates various attacks as a differentiable network layer to facilitate end-to-end training. The watermark data is diffused in a relatively wide area of the image to enhance security and robustness of the algorithm. Comparative results versus recent state-of-the-art researches highlight the superiority of the proposed framework in terms of imperceptibility, robustness and speed. The source codes of the proposed framework are publicly available at Github 1 .

ReDMark: Framework for Residual Diffusion Watermarking based on Deep Networks

Robust watermarking using orthogonal Fourier-Mellin moments and chaotic map for double images

This paper proposes a novel fractional transform, denoted as the fractional Krawtchouk transform (FrKT), a generalization of the Krawtchouk transform. The derivation of the FrKT uses the eigenvalue decomposition method. We determine the eigenvalues and the corresponding multiplicity of the Krawtchouk transform matrix. Moreover, the orthonormal eigenvectors of the transform matrix are derived. For validation purpose only and as a first illustration of the interest of FrKT, a watermarking example was chosen. Experimental results show that better watermark robustness and imperceptibility are achieved by adjusting the fractional orders in the FrKT.

Fractional Krawtchouk Transform With an Application to Image Watermarking

In order to transmit multiple-image synchronously, this paper introduces an encryption algorithm by combining equal modulus decomposition with quaternion gyrator transform. Firstly, each color image is encoded into a quaternion-valued matrix for a holistic processing. With the chaotic random phase mask, the quaternion gyrator spectrum is obtained. It is subsequently split into a complex-valued interim matrix and the equal modulus decomposition is performed to enhance the security, where the spectrum is divided into two complex-valued masks. Thereafter, the two set of phase masks are respectively superimposed and followed by gyrator transforms. Finally, a real-valued matrix is constructed as the final ciphertext image by splicing the real and imaginary parts together. The phase masks that are generated using the chaotic and real-valued ciphertext are convenient to storage and transmission. Moreover, the initial conditions are closely related with the plaintext images, which makes the cryptosystem achieve high security. Numerical simulations are made to demonstrate the reliability of the proposed cryptosystem.

Xilin Liu

Papers

Robust watermarking using orthogonal Fourier-Mellin moments and chaotic map for double images

Fractional Krawtchouk Transform With an Application to Image Watermarking

Multiple-image encryption based on chaotic phase mask and equal modulus decomposition in quaternion gyrator domain

Quaternion discrete fractional Krawtchouk transform and its application in color image encryption and watermarking

Content-adaptive image encryption with partial unwinding decomposition