航 周 | University of Science and Technology of China (original) (raw)
航 周
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Papers by 航 周
Reversible data hiding (RDH) schemes compete against each other for a sharply distributed predict... more Reversible data hiding (RDH) schemes compete against each other for a sharply distributed prediction error histogram, which is often realized by utilizing prediction strategies together with sorting techniques. The sorting technique aims to estimate the local context complexity for each pixel to optimize the embedding order. In this paper, we propose a novel second order perdicting and sorting technique for reversible data hiding. Firstly, the prediction error is obtained by an in-terchannel secondary prediction using the prediction errors of current channel and reference channel. Experiments show that this prediction method can produce a shaper second order prediction-error histogram. Then, we will introduce a novel second order perdicting-error sorting (SOPS) algorithm, which make full use of the feature of the edge information obtained from another color channel and high correlation between adjacent pixels. So it will reflect the texture complexity of current pixel better. Experimental results demonstrate that our proposed method out-performs the previous state-of-arts counterparts significantly in terms of both the prediction accuracy and the overall embedding performance.
Feature-based steganalysis has been an integral tool for detecting the presence of steganography ... more Feature-based steganalysis has been an integral tool for detecting the presence of steganography in communication channels for a long time. In this paper, we explore the possibility to utilize powerful optimization algorithms available in convolutional neural network packages to optimize the design of rich features. To this end, we implemented a new layer that simulates the formation of histograms from truncated and quantized noise residuals computed by convolution. Our goal is to show the potential to compactify and further optimize existing features, such as the projection spatial rich model (PSRM).
Reversible data hiding (RDH) schemes compete against each other for a sharply distributed predict... more Reversible data hiding (RDH) schemes compete against each other for a sharply distributed prediction error histogram, which is often realized by utilizing prediction strategies together with sorting techniques. The sorting technique aims to estimate the local context complexity for each pixel to optimize the embedding order. In this paper, we propose a novel second order perdicting and sorting technique for reversible data hiding. Firstly, the prediction error is obtained by an in-terchannel secondary prediction using the prediction errors of current channel and reference channel. Experiments show that this prediction method can produce a shaper second order prediction-error histogram. Then, we will introduce a novel second order perdicting-error sorting (SOPS) algorithm, which make full use of the feature of the edge information obtained from another color channel and high correlation between adjacent pixels. So it will reflect the texture complexity of current pixel better. Experimental results demonstrate that our proposed method out-performs the previous state-of-arts counterparts significantly in terms of both the prediction accuracy and the overall embedding performance.
Feature-based steganalysis has been an integral tool for detecting the presence of steganography ... more Feature-based steganalysis has been an integral tool for detecting the presence of steganography in communication channels for a long time. In this paper, we explore the possibility to utilize powerful optimization algorithms available in convolutional neural network packages to optimize the design of rich features. To this end, we implemented a new layer that simulates the formation of histograms from truncated and quantized noise residuals computed by convolution. Our goal is to show the potential to compactify and further optimize existing features, such as the projection spatial rich model (PSRM).