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The recent digital revolution has facilitated communication, data portability and on-the-fly manipulation. Unfortunately, this has brought along some critical security vulnerabilities that put digital documents at risk. The problem is in the security mechanism adopted to secure these documents by means of encrypted passwords; however, this security shield does not actually protect the documents which are stored intact. We propose here a solution to this real world problem through a 1D hash algorithm coupled with 2D iFFT (irreversible Fast Fourier Transform) to encrypt digital documents in the 2D spatial domain. Further by applying an imperceptible information hiding technique we can add another security layer which is resistant to noise and to a certain extent JPEG compression. We support this assertion by showing a practical example which is drawn from our set of experiments. This work exploits Jarvis’ kernel to generate the error diffusion signal and the Wavelet-based Inverse Halftoning via De-convolution (WInHD) to recover the approximation of the original signal. Our method not only points out forgery but also allows legal or forensics expert gain access to the original document despite being manipulated. This would undoubtedly be very useful in cases of disputes or claims. 相似文献
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《Journal of Visual Communication and Image Representation》2014,25(6):1378-1386
Achieving a high embedding capacity and low compression rate with a reversible data hiding method in the vector quantization (VQ) compressed domain is a technically challenging problem. This paper proposes a novel reversible steganographic scheme for VQ compressed images based on a locally adaptive data compression method. The proposed method embeds n secret bits into one VQ index of an index table in Hilbert-curve scan order. The experimental results show that the proposed method can achieve the different average embedding rates of 0.99, 1.68, 2.28, and 3.04 bit per index (bpi) and average compression rates of 0.45, 0.46, 0.5, and 0.56 bit per pixel (bpp) for n = 1, 2, 3, and 4, respectively. These results indicate that the proposed scheme is superior to Chang et al.’s scheme 1 [19], Yang and Lin’s scheme [21], and Chang et al.’s scheme 2 [24]. 相似文献
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Hsiu-Lien Yeh Shu-Tsai Gue Piyu Tsai Wei-Kuan Shih 《AEUE-International Journal of Electronics and Communications》2013,67(9):808-815
In this paper, a wavelet bit-plane based data hiding for compressed images is proposed. Image compression not only reduces storage but also benefits transmission. Currently, image encoders including JPEG2000, SPIHT, EZW, etc. also provide multi-stage encoding/decoding. In this paper, the bit-planes of DWT coefficients are selected to carry the secret image according to the multi-stage encoding. The hidden secret image can be extracted progressively from multi-stage decoded images.Experimental results showed that the secret image was embedded and extracted in multi-stage coded images. Furthermore, the structure of secret image could be identified in earlier decoding stages and then refined in later stages. Accordingly, the progressive secret revealing is achieved. In comparison with the other similar schemes, the proposed method achieves the better quality of stego-image than the other two when the hiding capacity is the same. 相似文献
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Designing efficient deep neural networks has achieved great interest in image super-resolution (SR). However, exploring diverse network structures is computationally expensive. More importantly, each layer in a network has a distinct role that leads to the design of a specialized structure. In this work, we present a novel neural architecture search (NAS) algorithm that efficiently explores layer-wise structures. Specifically, we construct a supernet allowing flexibility in choosing the number of channels and per-channel activation functions according to the role of each layer. The search process runs efficiently via channel pruning since gradient descent jointly optimizes the Mult-Adds and the accuracy of the searched models. We facilitate estimating the model Mult-Adds in a differentiable manner using relaxations in the backward pass. The searched model, named FGNAS, outperforms the state-of-the-art NAS-based SR methods by a large margin. 相似文献
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The latest deep neural networks for medical segmentation typically utilize transposed convolutional filters and atrous convolutional filters for spatial restoration and larger receptive fields, leading to dilution and inconsistency of visual semantics. To address such issues, we propose a novel attentional up-concatenation structure to build an auxiliary path for direct access to multi-level features. In addition, we employ a new structural loss to bring better morphological awareness and reduce the segmentation flaws caused by the semantic inconsistencies. Thorough experiments on the challenging optic cup/disc segmentation, cellular segmentation and lung segmentation tasks were performed to evaluate the proposed methods. Further ablation analysis demonstrated the effectiveness of the different components of the model and illustrated its efficiency. The proposed methods achieved the best performance and speed compared to the state-of-the-art models in three tasks on seven public datasets, including DRISHTI-GS, RIM-r3, REFUGE, MESSIDOR, TNBC, GlaS and LUNA. 相似文献
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Convolutional neural network (CNN) based methods have recently achieved extraordinary performance in single image super-resolution (SISR) tasks. However, most existing CNN-based approaches increase the model’s depth by stacking massive kernel convolutions, bringing expensive computational costs and limiting their application in mobile devices with limited resources. Furthermore, large kernel convolutions are rarely used in lightweight super-resolution designs. To alleviate the above problems, we propose a multi-scale convolutional attention network (MCAN), a lightweight and efficient network for SISR. Specifically, a multi-scale convolutional attention (MCA) is designed to aggregate the spatial information of different large receptive fields. Since the contextual information of the image has a strong local correlation, we design a local feature enhancement unit (LFEU) to further enhance the local feature extraction. Extensive experimental results illustrate that our proposed MCAN can achieve better performance with lower model complexity compared with other state-of-the-art lightweight methods. 相似文献
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隐秘信息能隐藏在网页标记字母中,虽在浏览器浏览时无法发现其存在,但却不可避免地改变了标记的内在特征标记偏移量。基于此,该文提出一种新的网页隐秘信息检测算法。根据标记偏移量在隐藏信息前和隐藏信息后的变换规律,确立高阶统计特征来检测网页标记中是否有隐秘信息。实验随机下载了30个不同类型网站的主页测试,实验结果验证了统计特征的正确性。检测的漏检率随嵌入信息的增大而减小,当50％的标记字母被用来隐藏信息后,检测的漏检率为0%。 相似文献
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Application of convolutional neural networks (CNNs) for image additive white Gaussian noise (AWGN) removal has attracted considerable attentions with the rapid development of deep learning in recent years. However, the work of image multiplicative speckle noise removal is rarely done. Moreover, most of the existing speckle noise removal algorithms are based on traditional methods with human priori knowledge, which means that the parameters of the algorithms need to be set manually. Nowadays, deep learning methods show clear advantages on image feature extraction. Multiplicative speckle noise is very common in real life images, especially in medical images. In this paper, a novel neural network structure is proposed to recover noisy images with speckle noise. Our proposed method mainly consists of three subnetworks. One network is rough clean image estimate subnetwork. Another is subnetwork of noise estimation. The last one is an information fusion network based on U-Net and several convolutional layers. Different from the existing speckle denoising model based on the statistics of images, the proposed network model can handle speckle denoising of different noise levels with an end-to-end trainable model. Extensive experimental results on several test datasets clearly demonstrate the superior performance of our proposed network over state-of-the-arts in terms of quantitative metrics and visual quality. 相似文献
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Compared with the traditional image denoising method, although the convolutional neural network (CNN) has better denoising performance, there is an important issue that has not been well resolved: the residual image obtained by learning the difference between noisy image and clean image pairs contains abundant image detail information, resulting in the serious loss of detail in the denoised image. In this paper, in order to relearn the lost image detail information, a mathematical model is deducted from a minimization problem and an end-to-end detail retaining CNN (DRCNN) is proposed. Unlike most denoising methods based on CNN, DRCNN is not only focus to image denoising, but also the integrity of high frequency image content. DRCNN needs less parameters and storage space, therefore it has better generalization ability. Moreover, DRCNN can also adapt to different image restoration tasks such as blind image denoising, single image superresolution (SISR), blind deburring and image inpainting. Extensive experiments show that DRCNN has a better effect than some classic and novel methods. 相似文献
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In this paper, a Covert Speech Telephone （CST） is designed and implemented based on the information hiding technique, which works on the internet. To solve the large embedding capacity problem for real-time information hiding, a steganographic system combined with a watermarking scheme is proposed, which skillfully transfers the secret speech into watermarking information. The basic idea is to use the speech recognition to significantly reduce the size of information that has to be transmitted in a hidden way. Furthermore, an improved DFT watermarking scheme is proposed which adaptively chooses the embedding locations and applies the multi-ary modulation technique. Based on the GUI （Graphical User Interface） software, the CST operates on both ordinary and secure mode. It is a completely digital system with high speech quality. Objective and subjective tests show that the CST is robust against normal signal processing attacks and steganalysis. The proposed scheme can be used in terms of military applications. 相似文献
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With the rapid development of mobile Internet and digital technology, people are more and more keen to share pictures on social networks, and online pictures have exploded. How to retrieve similar images from large-scale images has always been a hot issue in the field of image retrieval, and the selection of image features largely affects the performance of image retrieval. The Convolutional Neural Networks (CNN), which contains more hidden layers, has more complex network structure and stronger ability of feature learning and expression compared with traditional feature extraction methods. By analyzing the disadvantage that global CNN features cannot effectively describe local details when they act on image retrieval tasks, a strategy of aggregating low-level CNN feature maps to generate local features is proposed. The high-level features of CNN model pay more attention to semantic information, but the low-level features pay more attention to local details. Using the increasingly abstract characteristics of CNN model from low to high. This paper presents a probabilistic semantic retrieval algorithm, proposes a probabilistic semantic hash retrieval method based on CNN, and designs a new end-to-end supervised learning framework, which can simultaneously learn semantic features and hash features to achieve fast image retrieval. Using convolution network, the error rate is reduced to 14.41% in this test set. In three open image libraries, namely Oxford, Holidays and ImageNet, the performance of traditional SIFT-based retrieval algorithms and other CNN-based image retrieval algorithms in tasks are compared and analyzed. The experimental results show that the proposed algorithm is superior to other contrast algorithms in terms of comprehensive retrieval effect and retrieval time. 相似文献
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Screen content image (SCI) is a composite image including textual and pictorial regions resulting in many difficulties in image quality assessment (IQA). Large SCIs are divided into image patches to increase training samples for CNN training of IQA model, and this brings two problems: (1) local quality of each image patch is not equal to subjective differential mean opinion score (DMOS) of an entire image; (2) importance of different image patches is not same for quality assessment. In this paper, we propose a novel no-reference (NR) IQA model based on the convolutional neural network (CNN) for assessing the perceptual quality of SCIs. Our model conducts two designs solving problems which benefits from two strategies. For the first strategy, to imitate full-reference (FR) CNN-based model behavior, a CNN-based model is designed for both FR and NR IQA, and performance of NR-IQA part improves when the image patch scores predicted by FR-IQA part are adopted as the ground-truth to train NR-IQA part. For the second strategy, image patch qualities of one entire SCI are fused to obtain the SCI quality with an adaptive weighting method taking account the effect of the different image patch contents. Experimental results verify that our model outperforms all test NR IQA methods and most FR IQA methods on the screen content image quality assessment database (SIQAD). On the cross-database evaluation, the proposed method outperforms the existing NR IQA method in terms of at least 2.4 percent in PLCC and 2.8 percent in SRCC, which shows high generalization ability and high effectiveness of our model. 相似文献
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In this work, a framework that can automatically create cartoon images with low computation resources and small training datasets is proposed. The proposed system performs region segmentation and learns a region relationship tree from each learning image. The segmented regions are clustered automatically with an enhanced clustering mechanism with no prior knowledge of number of clusters. According to the topology represented by region relationship tree and clustering results, the regions are reassembled to create new images. A swarm intelligence optimization procedure is designed to coordinate the regions to the optimized sizes and positions in the created image. Rigid deformation using moving least squares is performed on the regions to generate more variety for created images. Compared with methods based on Generative Adversarial Networks, the proposed framework can create better images with limited computation resources and a very small amount of training samples. 相似文献
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基于失真函数的自适应隐写技术在嵌入过程中,忽略了嵌入操作相互间的影响,隐写策略无法随载体统计特性的改变自适应地调节。考虑嵌入操作的交互影响,该文提出一种基于动态更新失真代价的k隐写算法。首先分析了中心像素与其邻域的相关性,理论证明了在4-邻域修改情况下中心像素的最优修改方式,进而提出了失真代价更新策略MDS(Modification Degree Strategy);并结合该策略设计实现了一种自适应k隐写算法。实验表明,五元修改方式下算法UNIWARD-MDS(Pentary Version)在高嵌入率下(0.5~1.0 bpp)的抗SRM检测性优于S-UNIWARD(Pentary Version),同时在抵抗maxSRMd2检测时不同嵌入率下均优于S-UNIWARD(Pentary Version);三元修改方式下算法HILL-MDS和UNIWARD-MDS(Ternary Version)抗检测性能优于对应的自适应隐写算法HILL和S-UNIWARD(Ternary Version)。 相似文献