The Experts below are selected from a list of 1983 Experts worldwide ranked by ideXlab platform
Oscar C. Au - One of the best experts on this subject based on the ideXlab platform.
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Halftone Image watermarking by content aware double sided embedding error diffusion
IEEE Transactions on Image Processing, 2018Co-Authors: Oscar C. Au, Rui Wang, Lu FangAbstract:In this paper, we carry out a performance analysis from a probabilistic perspective to introduce the error diffusion-based Halftone visual watermarking (EDHVW) methods’ expected performances and limitations. Then, we propose a new general EDHVW method, content aware double-sided embedding error diffusion (CaDEED), via considering the expected watermark decoding performance with specific content of the cover Images and watermark, different noise tolerance abilities of various cover Image content, and the different importance levels of every pixel (when being perceived) in the secret pattern (watermark). To demonstrate the effectiveness of CaDEED, we propose CaDEED with expectation constraint (CaDEED-EC) and CaDEED-noise visibility function (NVF) and importance factor (IF) (CaDEED-N&I). Specifically, we build CaDEED-EC by only considering the expected performances of specific cover Images and watermark. By adopting the NVF and proposing the IF to assign weights to every embedding location and watermark pixel, respectively, we build the specific method CaDEED-N&I. In the experiments, we select the optimal parameters for NVF and IF via extensive experiments. In both the numerical and visual comparisons, the experimental results demonstrate the superiority of our proposed work.
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data hiding watermarking for Halftone Images
IEEE Transactions on Image Processing, 2002Co-Authors: Ming Sun Fu, Oscar C. AuAbstract:In many printer and publishing applications, it is desirable to embed data in Halftone Images. We proposed some novel data hiding methods for Halftone Images. For the situation in which only the Halftone Image is available, we propose data hiding smart pair toggling (DHSPT) to hide data by forced complementary toggling at pseudo-random locations within a Halftone Image. The complementary pixels are chosen to minimize the chance of forming visually undesirable clusters. Our experimental results suggest that DHSPT can hide a large amount of hidden data while maintaining good visual quality. For the situation in which the original multitone Image is available and the halftoning method is error diffusion, we propose the modified data hiding error diffusion (MDHED) that integrates the data hiding operation into the error diffusion process. In MDHED, the error due to the data hiding is diffused effectively to both past and future pixels. Our experimental results suggest that MDHED can give better visual quality than DHSPT. Both DHSPT and MDHED are computationally inexpensive.
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data hiding in Halftone Images by stochastic error diffusion
International Conference on Acoustics Speech and Signal Processing, 2001Co-Authors: Ming Sun Fu, Oscar C. AuAbstract:We propose a novel method called DHSED (data hiding stochastic error diffusion) to hide binary visual patterns in two error diffused Halftone Images. While one Halftone Image is only a regular error diffused Image, stochastic error diffusion is applied to the other Image to generate special stochastic characteristics with respect to the first Image such that the visual pattern would appear when the two Halftone Images are overlaid Simulation results show that the two Halftone Images have good visual quality, and the hidden pattern appears with "normal" and "lower-than-normal" intensity when the two Halftone Images are overlaid.
Jing-ming Guo - One of the best experts on this subject based on the ideXlab platform.
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Halftone Image security improving using overall minimal error searching
IEEE Transactions on Image Processing, 2011Co-Authors: Jing-ming Guo, Yunfu LiuAbstract:For Image-based data hiding, it is difficult to achieve good Image quality when high embedding capacity and 100% data extraction are also demanded. In this study, the proposed method, namely, overall minimal-error searching (OMES) is developed to meet the aforementioned requirements. Moreover, the concept of secret sharing is also adopted to distribute watermarks into multiple Halftone Images, and the embedded information can only be extracted when all of the marked Images are gathered. The OMES modifies the Halftone values at the same position of all host Images with the trained substitution table (S-Table). The S-Table makes the original combination of these Halftone values as another meaningful combination for embedding watermark, which is the key part in determining the Image quality. Thus, an optimization procedure is proposed to achieve the optimized S-Table. Two different encoders, called error-diffused-based and least-mean-square-based approaches are also developed to cooperate with the proposed OMES to cope with high processing speed and high Image quality applications, respectively. Finally, for resisting the issues caused by the print-and-scan attack, such as zooming, rotation, and dot gain effect, a compensation correction procedure is also proposed. As demonstrated in the experimental results, the proposed approach provides good Image quality, and is able to guard against some frequent happened attacks in printing applications.
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Halftone Image classification using lms algorithm and naive bayes
IEEE Transactions on Image Processing, 2011Co-Authors: Yunfu Liu, Jing-ming Guo, Jiannder LeeAbstract:Former research on inverse halftoning most focus on developing a general-purpose method for all types of Halftone patterns, such as error diffusion, ordered dithering, etc., while fail to consider the natural discrepancies among various halftoning methods. To achieve optimal Image quality for each halftoning method, the classification of Halftone Images is highly demanded. This study employed the least mean-square filter for improving the robustness of the extracted features, and employed the naive Bayes classifier to verify all the extracted features for classification. Nine of the most well-known halftoning methods were involved for testing. The experimental results demonstrated that the classification performance can achieve a 100% accuracy rate, and the number of distinguishable halftoning methods is more than that of a former method established by Chang and Yu.
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Watermarking in dithered Halftone Images with embeddable cells selection and inverse halftoning
Signal Processing, 2008Co-Authors: Jing-ming GuoAbstract:This work proposes a novel technique, named embeddable cells selection (ECS), for embedding flexible amounts of data in a ready-made dithered Halftone Image, while still achieving good quality results. The encoder embeds the secret data in the high-frequency regions with nearly the same number of black and white pixels and low pixel connections. A blind decoding technique without prior knowledge of the original dithered Image but with some side information is adopted in the decoder. The inverse halftoning and the second round of halftoning are the key steps in locating the embedded information bits. Experimental results demonstrate that an objective good quality Image with flexible capacity and reasonable complexity is obtained. Moreover, the correct decoding rate of 100% is maintained, and the original host Halftone Image can also be reconstructed in the decoder. Furthermore, by recording the embedded positions, this method can guard against distortions caused by tampering, cropping, and print-and-scan attacks.
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high capacity data hiding in Halftone Images using minimal error bit searching and least mean square filter
IEEE Transactions on Image Processing, 2006Co-Authors: Soo-chang Pei, Jing-ming GuoAbstract:In this paper, a high-capacity data hiding is proposed for embedding a large amount of information into Halftone Images. The embedded watermark can be distributed into several error-diffused Images with the proposed minimal-error bit-searching technique (MEBS). The method can also be generalized to self-decoding mode with dot diffusion or color Halftone Images. From the experiments, the embedded capacity from 33% up to 50% and good quality results are achieved. Furthermore, the proposed MEBS method is also extended for robust watermarking against the degradation from printing-and-scanning and several kinds of distortions. Finally, a least-mean square-based halftoning is developed to produce an edge-enhanced Halftone Image, and the technique also cooperates with MEBS for all the applications described above, including high-capacity data hiding with secret sharing or self-decoding mode, as well as robust watermarking. The results prove much sharper than the error diffusion or dot diffusion methods.
Wanjun Chen - One of the best experts on this subject based on the ideXlab platform.
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Sparsity-based inverse halftoning via semi-coupled multi-dictionary learning and structural clustering
Engineering Applications of Artificial Intelligence, 2018Co-Authors: Yan Zhang, Erhu Zhang, Wanjun Chen, Yajun Chen, Jinghong DuanAbstract:Abstract Inverse halftoning is the restoration of a continuous-tone Image from its Halftone version, which is a critical process for Halftone transform, digital archive management and high precision identification of Halftone. In this paper, a novel inverse halftoning method based on semi-coupled multi-dictionary learning is proposed to address the cross-style Image restoration from Halftone Images to continuous-tone Images. By using semi-coupled multi-dictionary learning, multiple dictionary pairs and their corresponding mapping functions between continuous-tone Image and its Halftone version could be simultaneously learned. The learned multiple dictionary pairs can well represent the structure characteristics of Halftone Images and continuous-tone Images, respectively. In addition, the mapping functions learned by semi-coupled manner can bridge the gap between the two different style Images of Halftone Image and continuous-tone Image. Unlike the existed methods, the proposed method could effectively relax the assumption of the same sparse coding coefficients in coupled dictionary learning. To obtain more accurate mapping functions, a structural clustering method for cross-style Image patches is proposed by using SUSAN (smallest univalue segment assimilating nucleus) filtering and HOG (histogram of oriented gradient) features, which can capture the similar structure features from Halftone Images and continuous-tone Images, and thus improve the classification accurate rate of Halftone Image patches. The experimental results demonstrate that the proposed method can restore higher quality continuous-tone Images than that produced by the state-of-the-art methods, which not only reduce the screen noise in smooth regions, but also provide well fine details and clear edges.
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deep neural network for Halftone Image classification based on sparse auto encoder
Engineering Applications of Artificial Intelligence, 2016Co-Authors: Yan Zhang, Erhu Zhang, Wanjun ChenAbstract:To restore high quality continuous tone Images from each class of Halftone Images, Halftone Image fine classification is the key problem. In this paper, a novel feature learning method is proposed for classifying 14 kinds of Halftone Images produced by the most well-known halftoning algorithms. This study employs the stacked sparse auto-encoders (SAE) trained with unsupervised learning for extracting features of Halftone Images, and then uses softmax regression with supervised learning for fine-tuning the deep neural network and classifying Halftone Images. In order to reduce the run-time of deep neural network and improve the Image correct classification rate, we propose an effective patch extraction method for testing Halftone Images by measuring the mean and variance of local entropy in a patch. Halftone Image classification is determined by the classification results of all effective patches inside an Image via majority voting (MV). The experimental results demonstrate that our proposed method achieves an average correct classification rate (ACCR) of over 99.44% for 14 kinds of Halftone Images on two public Image sets. Compared with state-of-the-art LMS-Bayes and M 10 - ML methods, the proposed SAE-MV method can distinguish the most categories of Halftone Images and achieve competitive ACCR, meanwhile, demonstrate better generalization performance. HighlightsA Halftone Image classification algorithm is proposed by using deep neural network.The intrinsic features of Halftone Images are extracted by the sparse auto-encoders.The effective patch extraction saves time cost and improves classification accuracy.The algorithm has superior classification accuracy and generalization performance.
Yan Zhang - One of the best experts on this subject based on the ideXlab platform.
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Sparsity-based inverse halftoning via semi-coupled multi-dictionary learning and structural clustering
Engineering Applications of Artificial Intelligence, 2018Co-Authors: Yan Zhang, Erhu Zhang, Wanjun Chen, Yajun Chen, Jinghong DuanAbstract:Abstract Inverse halftoning is the restoration of a continuous-tone Image from its Halftone version, which is a critical process for Halftone transform, digital archive management and high precision identification of Halftone. In this paper, a novel inverse halftoning method based on semi-coupled multi-dictionary learning is proposed to address the cross-style Image restoration from Halftone Images to continuous-tone Images. By using semi-coupled multi-dictionary learning, multiple dictionary pairs and their corresponding mapping functions between continuous-tone Image and its Halftone version could be simultaneously learned. The learned multiple dictionary pairs can well represent the structure characteristics of Halftone Images and continuous-tone Images, respectively. In addition, the mapping functions learned by semi-coupled manner can bridge the gap between the two different style Images of Halftone Image and continuous-tone Image. Unlike the existed methods, the proposed method could effectively relax the assumption of the same sparse coding coefficients in coupled dictionary learning. To obtain more accurate mapping functions, a structural clustering method for cross-style Image patches is proposed by using SUSAN (smallest univalue segment assimilating nucleus) filtering and HOG (histogram of oriented gradient) features, which can capture the similar structure features from Halftone Images and continuous-tone Images, and thus improve the classification accurate rate of Halftone Image patches. The experimental results demonstrate that the proposed method can restore higher quality continuous-tone Images than that produced by the state-of-the-art methods, which not only reduce the screen noise in smooth regions, but also provide well fine details and clear edges.
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deep neural network for Halftone Image classification based on sparse auto encoder
Engineering Applications of Artificial Intelligence, 2016Co-Authors: Yan Zhang, Erhu Zhang, Wanjun ChenAbstract:To restore high quality continuous tone Images from each class of Halftone Images, Halftone Image fine classification is the key problem. In this paper, a novel feature learning method is proposed for classifying 14 kinds of Halftone Images produced by the most well-known halftoning algorithms. This study employs the stacked sparse auto-encoders (SAE) trained with unsupervised learning for extracting features of Halftone Images, and then uses softmax regression with supervised learning for fine-tuning the deep neural network and classifying Halftone Images. In order to reduce the run-time of deep neural network and improve the Image correct classification rate, we propose an effective patch extraction method for testing Halftone Images by measuring the mean and variance of local entropy in a patch. Halftone Image classification is determined by the classification results of all effective patches inside an Image via majority voting (MV). The experimental results demonstrate that our proposed method achieves an average correct classification rate (ACCR) of over 99.44% for 14 kinds of Halftone Images on two public Image sets. Compared with state-of-the-art LMS-Bayes and M 10 - ML methods, the proposed SAE-MV method can distinguish the most categories of Halftone Images and achieve competitive ACCR, meanwhile, demonstrate better generalization performance. HighlightsA Halftone Image classification algorithm is proposed by using deep neural network.The intrinsic features of Halftone Images are extracted by the sparse auto-encoders.The effective patch extraction saves time cost and improves classification accuracy.The algorithm has superior classification accuracy and generalization performance.
Wanteng Liu - One of the best experts on this subject based on the ideXlab platform.
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Deep residual network for Halftone Image steganalysis with stego-signal diffusion
Signal Processing, 2020Co-Authors: Lingwen Zeng, Wanteng Liu, Junjia ChenAbstract:Abstract More and more convolution neural network (CNN) models are used for Image steganalysis, which show superior performances than traditional steganalytic methods. However, no researches on Halftone Image steganalysis by CNN have yet been carried. In this paper, a novel residual CNN model with stego-signal diffusion for Halftone Image steganalysis is proposed and achieves state-of-the-art detection accuracy. Considering inverse halftoning can reconstruct the gray-scale Image from the Halftone Image, inverse halftoning is used to preprocess the Halftone Image, which can diffuse the stego-signal to neighboring pixels. As a result, the difference between the cover and stego Image is magnified on the texture. Then, the residual block is utilized to construct the CNN model, since it could preserve the stego-signal better than plain network, and the magnified difference allows the network to better identify cover and stego Images. A series of experiments are conducted on a large-scale dataset. The detection accuracy is improved by the magnified difference, and our proposed model outperforms the previous methods.
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secure Halftone Image steganography with minimizing the distortion on pair swapping
Signal Processing, 2020Co-Authors: Wanteng Liu, Xiaolin Yin, Junhong Zhang, Jinhua Zeng, Shaopei Shi, Mingzhi MaoAbstract:Abstract In Halftone Image data hiding, pixel pairs containing master pixels and slave pixels are common operating units. In most previous researches, master pixels are selected at a set of pseudo-random locations, which degrade the Image quality. In this paper, a secure Halftone Image steganographic scheme based on pair swapping is presented, which aims at minimizing the embedding distortions. Different from most previous researches, there is no master-slave relationship in the proposed scheme and the steganographic performance depends on the selection of pixel pairs instead of slave pixels. Based on a human visual system (HVS) model of Halftone Images, the superiority of pair swapping is proved and vertical swapping is further demonstrated to be the optimal way to improve visual quality among all pair swapping strategies. Finally, a statistical model is developed to predict the vertical pair pattern by considering its neighboring region, based on which, a distortion measurement is proposed to evaluate the embedding distortions on both vision and statistics. To play the advantage of the distortion measurement, syndrome-trellis code (STC) is employed to minimize the embedding distortions. Experimental results show that the proposed steganographic scheme achieves high statistical security with high embedding capacity without degrading the visual quality.
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efficient Halftone Image steganography based on dispersion degree optimization
Journal of Real-time Image Processing, 2019Co-Authors: Yingjie Xue, Wanteng Liu, Yuileong Yeung, Xianjin Liu, Hongmei LiuAbstract:Halftone Images are usually used in facsimile and Halftone Image steganography can be used for facsimile channel. In recent years, real-time Image processing becomes more and more important. In this paper, an efficient block-based steganographic method for Halftone Images is proposed. This method is based on optimal dispersion degree (DD), which can measure the complexity of the region texture. To reduce the visual distortion, the blocks with complex texture can be selected as carriers according to the dispersion degree. Finally, the secret messages are embedded by flipping the pixels that can minimize the changes of texture structure. The experiments demonstrate that the proposed scheme maintains a good Image visual quality and realizes acceptable statistical security with high capacity.
