A blind medical image watermarking: DWT-SVD based robust and secure approach for telemedicine applications

Multimedia Tools and Applications - In this paper, a blind image watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed. In this scheme,...     

基于神经网络分类的图像水印算法

将人工神经网络（ANN）、广义猫映射及概率统计等知识相结合构造了一种图像空间域水印算法。采用神经网络作为载体图像的纹理分类器,突出原始图像的纹理区。使用广义猫映射对水印进行置乱预处理,提高了水印信息的安全性。水印嵌入时采用最小化像素改变的优化策略,提取时应用概率统计等知识较好地实现了水印信息不可见性和鲁棒性的统一。实验结果表明,该方法能有效地抵抗剪切攻击、噪声攻击、最低有效位（LSB）攻击、滤波攻击等。     

DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems

Deep Neural Networks (DNNs) are compute-intensive learning models with growing applicability in a wide range of domains. Due to their computational complexity, DNNs benefit from implementations that utilize custom hardware accelerators to meet performance and response time as well as classification accuracy constraints. In this paper, we propose DeepMaker framework that aims to automatically design a set of highly robust DNN architectures for embedded devices as the closest processing unit to the sensors. DeepMaker explores and prunes the design space to find improved neural architectures. Our proposed framework takes advantage of a multi-objective evolutionary approach that exploits a pruned design space inspired by a dense architecture. DeepMaker considers the accuracy along with the network size factor as two objectives to build a highly optimized network fitting with limited computational resource budgets while delivers an acceptable accuracy level. In comparison with the best result on the CIFAR-10 dataset, a generated network by DeepMaker presents up to a 26.4x compression rate while loses only 4% accuracy. Besides, DeepMaker maps the generated CNN on the programmable commodity devices, including ARM Processor, High-Performance CPU, GPU, and FPGA.     

Advanced metaheuristic optimization techniques in applications of deep neural networks: a review

Neural Computing and Applications - Deep neural networks (DNNs) have evolved as a beneficial machine learning method that has been successfully used in various applications. Currently, DNN is a...     

基于深度神经网络的行人头部检测

行人检测已成为安防、智能视频监控、景区人流量统计所依赖的核心技术,最新目标检测方法包括快速的区域卷积神经网络Fast RCNN、单发多重检测器 SSD、部分形变模型DPM等,皆为对行人整体的检测。在大场景下,行人姿态各异,物体间遮挡频繁,只有通过对行人身体部分位置建模,抓住人的局部特征,才能实现准确的定位。利用Faster RCNN深度网络原型,针对行人头部建立检测模型,同时提取行人不同方向的头部特征,并加入空间金字塔池化层,保证检测速率,有效解决大场景下行人的部分遮挡问题,同时清晰地显示人群大致流动方向,相比普通的人头估计,更有利于人流量统计。     

Multi-level security of medical images based on encryption and watermarking for telemedicine applications

Multimedia Tools and Applications - In this paper, a robust and hybrid domain watermarking scheme is proposed for the security of medical images in telemedicine applications. The secret identity of...     

Plant identification based on very deep convolutional neural networks

Multimedia Tools and Applications - Plant identification is a critical step in protecting plant diversity. However, many existing identification systems prohibitively rely on hand-crafted features...     

基于DWT和RBF神经网络的水印算法研究

提出了一种在经过四级小波变换的原始图像中嵌入数字水印的算法.根据人类视觉特征来决定嵌入水印的强度,用秘钥来决定水印嵌入的位置,通过使用训练的RBF神经网络来嵌入和提取水印.实验结果表明:该算法简单有效,且对常见的图像处理具有较好的鲁棒性.     

Background subtraction based on deep convolutional neural networks features

Multimedia Tools and Applications - Background modeling and subtraction, the task to detect moving objects in a scene, is a fundamental and critical step for many high level computer vision tasks....     

基于深度卷积神经网络的物体识别算法

针对传统物体识别算法中人工设计出来的特征易受物体形态多样性、光照和背景的影响,提出了一种基于深度卷神经网络的物体识别算法。该算法基于NYU Depth V2场景数据库,首先将单通道深度信息转换为三通道;再用训练集中的彩色图片和转换后的三通道深度图片分别微调两个深度卷积神经网络模型;然后用训练好的模型对重采样训练集中的彩色和深度图片提取模型第一个全连接层的特征,并将两种模态的特征串联起来,训练线性支持向量机（LinSVM）;最后将所提算法应用到场景理解任务中的超像素特征提取。所提方法在测试集上的物体分类准确度可达到91.4%,比SAE-RNN方法提高4.1个百分点。实验结果表明所提方法可提取彩色和深度图片高层特征,有效提高物体分类准确度。     

FusionCNN: a remote sensing image fusion algorithm based on deep convolutional neural networks

Multimedia Tools and Applications - In remote sensing image fusion field, traditional algorithms based on the human-made fusion rules are severely sensitive to the source images. In this paper, we...     

Probabilistic design of layered neural networks based on theirunified framework

Proposes three ways of designing artificial neural networks based on a unified framework and uses them to develop new models. First, the authors show that artificial neural networks can be understood as probability density functions with parameters. Second, the authors propose three design methods for new models: a method for estimating the occurrence probability of the inputs, a method for estimating the variance of the outputs, and a method for estimating the simultaneous probability of inputs and outputs. Third, the authors design three new models using the proposed methods: a neural network with occurrence probability estimation, a neural network with output variance estimation, and a probability competition neural network. The authors' experimental results show that the proposed neural networks have important abilities in information processing; they can tell how often a given input occurs, how widely the outputs are distributed, and from what kinds of inputs a given output is inferred.     

DeepEC: An error correction framework for dose prediction and organ segmentation using deep neural networks

Radiotherapy is an indispensable part of adjuvant therapy for cancer that improves local control, overall survival, and the opportunity for good quality of life. Organ delineation and dose plan design are the key steps in the treatment. Organ delineation controls the area of radiotherapy and dose planning controls its intensity. However, both tasks are time-consuming, exhausting, and subjective, and automated methods are desirable. Although automated methods have been studied, the previous studies either focus on organ segmentation or dose prediction, without considering them from a holistic perspective. In this paper, we treat organ segmentation and dose prediction as similar tasks, and propose an error correction framework to improve their performance based on the same mechanism. The proposed error correction framework consists of a prediction network and a calibration network. The biggest difference between our framework and previous studies is that the state-of-the-art networks can be used as a prediction network or calibration network, and then the performance can be improved by the error correction mechanism. To evaluate the framework, we conducted a series of experiments on dose prediction and organ segmentation. These experimental results show that the framework is superior to other state-of-the-art methods in both tasks.     

基于平衡多小波的改进的盲水印算法

利用图像经过CARDBAL平衡多小波变换(BMWT)后,能量汇聚且平均分摊在最低分辨率子图像4个分量上的特点,提出了一种基于BMWT和DCT的改进的盲水印算法.在彩色图像的饱和度分量上嵌入一幅二值水印图像,水印嵌入前进行Arnold置乱和混沌加密预处理,保证了算法的安全性.实验结果表明,改进后的算法具有良好的鲁棒性,而且在抵抗JPEG压缩性能方面有明显的提高.     

一种利用反插值操作的隐蔽中毒攻击方法

为了解决图像的预处理操作造成中毒样本的触发器消失不见或被破坏,导致攻击性能失效的问题,提出一种利用反插值操作的隐蔽中毒攻击方法.通过对尺寸缩放后的目标图像进行反插值计算,实现针对性的中毒图像优化.该中毒图像可在尺寸缩放后变为带有特定触发器的目标图像并输入模型中训练,在模型中插入后门,实现对模型的中毒攻击.实验针对MNIST和CIFAR10数据集展开中毒攻击实验,与现有方法相比,所提出方法能够在保持中毒攻击成功率基本不变的同时,中毒过程更隐蔽.     

Wavelet-based image watermarking with visibility range estimation based on HVS and neural networks

This work proposes a wavelet-based image watermarking (WIW) technique, based on the human visible system (HVS) model and neural networks, for image copyright protection. A characteristic of the HVS, which is called the just noticeable difference (JND) profile, is employed in the watermark embedding to enhance the imperceptibility of the technique. First, we derive the allowable visibility ranges of the JND thresholds for all coefficients of a wavelet-transformed image. The WIW technique exploits the ranges to compute the adaptive strengths to be superimposed in the wavelet coefficients while embedding watermarks. An artificial neural network (ANN) is then used to memorize the relationships between the original wavelet coefficients and its watermark version. Consequently, the trained ANN is utilized for estimating the watermark without the original image. Many existing schemes require the original image to be involved in the calculation of the JND profile of the image. Finally, computer simulations demonstrate that both transparency and robustness of the WIW technique are superior to that of other proposed methods.     

Stratified pooling based deep convolutional neural networks for human action recognition

Video based human action recognition is an active and challenging topic in computer vision. Over the last few years, deep convolutional neural networks (CNN) has become the most popular method and achieved the state-of-the-art performance on several datasets, such as HMDB-51 and UCF-101. Since each video has a various number of frame-level features, how to combine these features to acquire good video-level feature becomes a challenging task. Therefore, this paper proposed a novel action recognition method named stratified pooling, which is based on deep convolutional neural networks (SP-CNN). The process is mainly composed of five parts: (i) fine-tuning a pre-trained CNN on the target dataset, (ii) frame-level features extraction; (iii) the principal component analysis (PCA) method for feature dimensionality reduction; (iv) stratified pooling frame-level features to get video-level feature; and (v) SVM for multiclass classification. Finally, the experimental results conducted on HMDB-51 and UCF-101 datasets show that the proposed method outperforms the state-of-the-art.     

Framework for robust blind image watermarking based on classification of attacks

With the widespread internet usage, digital contents are easily distributed throughout the world. To eliminate concerns of producers and owners of digital contents, watermarking techniques are extensively being used. Robustness against intentional and unintentional attacks is a major quality of watermarking systems. Since different attacks tend to target different parts of the frequency spectrum, in this paper we propose a framework for blind watermarking which determines the type of attack that the image has gone through before extracting the watermark. Within this framework, we propose an attack classification method to identify the region of the frequency spectrum that is less damaged. The watermark which is redundantly spread throughout the spectrum can be extracted from the less damaged regions. Experimental results show functionality of the framework by producing better results in comparison with well-known blind watermarking techniques.     

Region-based hybrid medical image watermarking for secure telemedicine applications

In this paper we propose a novel region based hybrid medical image watermarking (MIW) scheme to ensure authenticity, integrity and confidentiality of medical images. In this scheme a digital medical image is partitioned into region of interest (ROI) and the region of non interest (RONI). To detect and localize ROI tampering with high accuracy pixel wise positional and relational bits are calculated. Positional bit is calculated with respect to MSBs, row and column of the pixel. Relational bit shows the relation between MSBs. Two original LSBs of each ROI pixel are replace by their corresponding positional and relational bits. Original LSBs of ROI pixels are concatenated and embedded in RONI for ROI recovery in the case of tampering. Multiple watermarks i.e. electronic patient record (EPR), hospitals logo and LSBs of ROI are embedded simultaneously as a robust watermark in RONI using IWT-SVD hybrid transform. The proposed scheme is blind and free from false positive detection. Various experiments have been carried out on different medical imaging modalities to evaluate the performance of the proposed scheme in terms of imperceptibility, robustness, tamper detection, localization, recovery and computation time. ROI tampering is detected and recovered with high accuracy. Thus, the proposed scheme is effective in telemedicine applications.