Perceptual hash-based coarse-to-fine grained image tampering forensics method

As an active forensic technology, perceptual image hash has important application in image content authenticity detection and integrity authentication. In this paper, we propose a hybrid-feature-based perceptual image hash method that can be used for image tampering detection and tampering localization. In the proposed method, we use the color features of image as global features, use point-based features and block-based features as local features, and combine with the structural features to generate intermediate hash code. Then we encrypt and randomize to generate the final hash code. Using this hash code, we present a coarse-to-fine grained forensics method for image tampering detection. The proposed method can realize object-level tampering localization. Abundant experimental results show that the proposed method is sensitive to content changes caused by malicious attacks, and the tampering localization precision achieves pixel level, and it is robust to a wide range of geometric distortions and content-preserving manipulations. Compared with the state-of-the-art schemes, the proposed scheme yields superior performance.     

颜色对比度增强的红外与可见光图像融合方法

针对红外与可见光彩色融合图像中目标与背景间的低对比度的问题,提出一种基于HSI空间颜色对比度增强的红外和可见光图像融合方法.首先对输入的可见光与红外图像进行直方图均衡和中值滤波加强处理,然后对加强的红外图像模糊阈值分割得到红外目标,最后把分割的红外目标图像和加强的可见光和红外图像在HSI空间的三通道线性融合和色彩传递,为了增强目标与背景间的颜色对比度,在色彩传递阶段, H通道的色彩传递方程中引入一个比例因子.实验结果表明:与其他算法相比,该方法得到的彩色融合图像热目标和低温物体与背景间的颜色对比度明显加强,同时背景的细节信息呈现白天类似的自然彩色,更加符合人眼视觉感知.     

Optimized hierarchical block matching for fast and accurate image registration

Recently the camera resolution has been highly increased, and the registration between high-resolution images is computationally expensive even by using hierarchical block matching. This paper presents a novel optimized hierarchical block matching algorithm in which the computational cost is minimized for the scale factor and the number of levels in the hierarchy. The algorithm is based on a generalized version of the Gaussian pyramid and its inter-layer transformation of coordinates. The search window size is properly determined to resolve possible error propagation in hierarchical block matching. In addition, we also propose a simple but effective method for aligning colors between two images based on color distribution adjustment as a preprocessing. Simplifying a general color imaging model, we show much of the color inconsistency can be compensated by our color alignment method. The experimental results show that the optimized hierarchical block matching and color alignment methods increase the block matching speed and accuracy, and thus improve image registration. Using our algorithm, it takes about 1.28 s for overall registration process with a pair of images in 5 mega-pixel resolution.     

基于NSST和高斯混合模型的医学图像融合算法

提出了一种基于非下采样剪切波变换（Non-subsampled shearlet transform, NSST）和高斯混合模型的医学彩色图像融合算法。首先将彩色图像转换到HSI颜色空间,提取其色度分量图像、饱和度分量图像和强度分量图像;然后对强度分量图像和MRI图像进行NSST变换,其中低频系数采用基于区域系数改进拉普拉斯能量和（Sum-modified-Laplacian,SML）加权的融合规则,高频系数采用高斯混合模型估计参数取大的融合规则;对融合后的系数进行NSST逆变换重构融合后的强度分量图像;最后将融合后的强度分量图像与色度分量图像、饱和度分量图像混合得到融合后的HSI图像,再将HSI图像转换到RGB颜色空间得到融合后的彩色图像。仿真实验表明,该算法在视觉效果和客观评价指标上具有更好的融合效果。     

对局部内容篡改敏感的感知图像散列

提出了一种基于图像内容和颜色分布的感知图像散列.先将图像尺寸规格化并分成小块,根据各块亮度矩阵的奇异值判断其是否属于复杂区域,由此得到复杂区分布索引表.计算各图像块 Y 分量的均值和 R、G、B均值两两之差的最小值,构成表征亮度和颜色分布的特征向量,将它与复杂区索引组合并加密得到图像散列.实验结果表明,由此提取的图像散列对保持图像内容不变的JPEG压缩、平滑滤波、缩放等处理具有良好的稳健性,而对内容篡改敏感并能准确定位篡改部位.     

Quaternion pseudo-Zernike moments combining both of RGB information and depth information for color image splicing detection

The quaternion representation (QR) used in current quaternion-based color image processing creates redundancy when representing a color image of three components by a quaternion matrix having four components. In this paper, both RGB and depth (RGB-D) information are considered to improve QR for efficiently representing RGB-D images. The improved QR fully utilizes the four-dimensional quaternion domain. Using this improved QR, firstly we define the new quaternion pseudo-Zernike moments (NQPZMs) and then propose an efficient computational algorithm for NQPZMs through the conventional pseudo-Zernike moments (PZMs). Finally, we propose an algorithm for color image splicing detection based on the NQPZMs and the quaternion back-propagation neural network (QBPNN). Experimental results on four public datasets (DVMM, CASIA v1.0 and v2.0, Wild Web) demonstrate that the proposed splicing detection algorithm can achieve almost 100% accuracy with the appropriate feature dimensionality and outperforms 14 existing algorithms. Moreover, the comparison of six color spaces (RGB, HSI, HSV, YCbCr, YUV, and YIQ) shows that the proposed algorithm using YCbCr color space has the overall best performance in splicing detection.     

十种基于颜色特征图像检索算法的比较和分析

在基于内容的图像检索中,颜色特征已得到广泛应用.本文对十种利用颜色特征进行图像检索的算法利用同一图像库进行了实验比较.实验结果表明,无论在HSI空间或MTM空间,累加直方图法均优于一般直方图法,对这一点本文还首次给出了严格的理论证明.实验结果还表明,加权距离法比欧氏距离法总体上没有明显改善,MTM空间比HSI空间也没有显出优势,而中心矩法算法简单,检索速度快,通过调整加权系数,检索精度可以接近累加直方图法.本文的实验和分析对选择和优化检索算法有一定的参考价值.     

一种改进的火灾颜色模型检测算法

从火焰的颜色特征出发,综合考虑已有颜色模型的优缺点,提出一种改进的基于颜色模型的火灾检测方法.首先结合HSI颜色模型和YCbCr颜色模型对火灾图像进行预处理提取出可能火焰区域,然后在HSI空间上采用颜色空间距离法去除噪声.实验结果表明,该方法提高了火灾图像识别的准确度,并能在一定程度上消除干扰,具有较好的适用性.     

一种改进的综合BTC颜色矩和DT-CWT的图像检索算法

摘 要：特征提取是基于内容的图像检索中的关键技术。针对基于单一特征检索效果不理想的问题,提出一种改进的综合颜色和纹理特征的图像检索算法。该算法在YIQ颜色空间中进行特征提取,首先结合方块编码（BTC）的思想,提取颜色矩作为颜色特征;采用双树复小波变换（DT-CWT）提取纹理特征,融合两种特征并利用相似性度量方式进行图像检索。实验结果表明算法所提取的颜色、纹理特征更利于检索,使用综合特征检索的平均查准率比同类算法更高。     

一种肤色定位的人脸检测算法

进行人脸识别前,首先要精确定位出一幅图像中人脸的位置,为了快速定位人脸位置,本文提出一种肤色定位的人脸检测算法。首先将实际获取的彩色图像转换为YCbCr和HSI空间图像,并将Cb和Cr图像中的数值进行四舍五入处理,结合Cb、Cr、H和S的阈值去除大部分背景,再统计当前图像中的彩色部分的Cb和Cr值,分别取最多2个数值来共同确定肤色位置,最后由当前位置的亮度信息图像排除手等纯肤色部分,准确定位人脸。本文算法能提高正检率并降低误检率,有利于人脸检测。     

A new Copy-Move forgery detection method using LIOP

The most prevalent type of digital image falsification occurs when a portion of a image is copied and pasted onto another section of the same image. Falsification of the image made in this way is called copy-move forgery (CMF). This study presents a new and effective approach for copy-move forgery detection (CMFD) using the Local Intensity Order Pattern (LIOP) to overcome the restrictions of existing CMFD techniques. The input image is first converted to a YCbCr color space and then split into Y, Cb, and Cr color channels. The LIOP features are then extracted from each color channel and all the features are combined. The feature vectors are ordered lexicographically and related features are detected by comparing the LIOP features. Although the LIOP feature has rarely been used in CMFD prior to this study, the success rate of the proposed method is high. In addition, since the channels are not correlated to each other in the YCbCr color space, each color channel is considered as a gray image, and the success rate is increased by combining the features extracted from each of the color channels. The proposed approach was assessed using the CoMoFoD and GRIP datasets. Experimental findings demonstrated that the suggested method was successful and displayed robustness in post-processing attacks.     

基于HSI梯度统计特性的图像修复算法

为更好保持修复后图像的结构连贯性及与邻域信 息的连续一致性,提出了基于HSI梯度统计特性的图像修复算法。 该方法首先在HSI色彩空间提取图像的梯度特征,利用梯度均值和方差等统计量重新定义样 本块的结构相似性,在此基 础上构造加权的数据项和平滑项的全局能量约束方程,同时自适应地确定计算全局能量的样 本块尺寸和多分辨率层数,并 利用图割算法进行最优化求解。由于HSI色彩空间能更加充分地提取图像的梯度特征,利用 样本块HSI梯度统计特性使 得搜寻得到的匹配标签更加合理。实验结果表明,该算法对于自然图像中小区域破损和目标 物体移除方面有较好的应用, 修复结果很好地保持了图像结构的连贯性,并削弱了接缝效应,更符合人眼视觉要求。     

基于最大隶属度与SVM的彩色图像分割新算法研究

图像分割是图像处理的主要问题,也是图像处理的难题之一,由于彩色图像提供了比灰度图像更多的信息,因此彩色图像处理,尤其是彩色图像分割受到了越来越多的重视。本论文提出一种基于HSI模型和模糊最大隶属度原则与SVM的彩色图像分割方法,从彩色图像的彩色直方图中获取目标色RGB模型和背景色RGB模型,再将RGB模型转化为HSI模型建立一组色彩模糊集,对HSI模型进行SVM训练识别再计算该图像中的色彩在各模糊集中的隶属度,最后确定色彩的归属,再将HSI模型转化为RGB模型。实现了快速有效的彩色图像分割方法的研究。     

Correlation-based approach to color image compression

Most coding techniques for color image compression employ a de-correlation approach—the RGB primaries are transformed into a de-correlated color space, such as YUV or YCbCr, then the de-correlated color components are encoded separately. Examples of this approach are the JPEG and JPEG2000 image compression standards. A different method, of a correlation-based approach (CBA), is presented in this paper. Instead of de-correlating the color primaries, we employ the existing inter-color correlation to approximate two of the components as a parametric function of the third one, called the base component. We then propose to encode the parameters of the approximation function and part of the approximation errors. We use the DCT (discrete cosine transform) block transform to enhance the algorithm's performance. Thus the approximation of two of the color components based on the third color is performed for each DCT subband separately. We use the rate-distortion theory of subband transform coders to optimize the algorithm's bits allocation for each subband and to find the optimal color components transform to be applied prior to coding. This pre-processing stage is similar to the use of the RGB to YUV transform in JPEG and may further enhance the algorithm's performance. We introduce and compare two versions of the new algorithm and show that by using a Laplacian probability model for the DCT coefficients as well as down-sampling the subordinate colors, the compression results are further improved. Simulation results are provided showing that the new CBA algorithms are superior to presently available algorithms based on the common de-correlation approach, such as JPEG.     

Image quality assessment based on adaptive multiple Skyline query

Non-reference image quality assessment has attracted great emphasis in recent years. Traditional image quality assessment algorithms based on structural similarity cannot make full use of the image gradient features, and the contrast similarity features often ignore the consistency of continuous color blocks within the image, which leads to large discrepancy between the evaluation result and the subjective judgment of human vision system. In this paper, we propose a deep model for image quality assessment where the spatial and visual features of image are both considered. For better feature fusion, we design an adaptive multiple Skyline query algorithm named MSFF, which takes as input multiple features of images, and learns the feature weights through end-to-end training. Extensive experiments on image quality assessment tasks prove that the proposed model exhibits superior performance compared with existing solutions.     

Deep quality assessment toward defogged aerial images

In order to improve the visual appearance of defogged of aerial images, in this work, a novel defogging algorithm based on conditional generative adversarial network is proposed. More specifically, the training process is carried out through an end-to-end trainable deep neural network. In detail, we upgrade the traditional adversarial loss function by incorporating an L1-regularized gradient to encode a rich set of detailed visual information inside each aerial image. In practice, to our best knowledge, existing image quality assessment algorithms might have deviation and supersaturation distortion on aerial images. To alleviate this problem, we leverage a random forest classification model to learn the mapping relationship between aerial image features and the quality ranking results. Subsequently, we transform the objective of defogged image quality assessment into a classification problem. Comprehensive experimental results on our compiled fogged aerial images quality data set have clearly demonstrated the effectiveness of our proposed algorithm.     

Research on image feature extraction and retrieval algorithms based on convolutional neural network

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.     

基于分块颜色直方图和GWLBP的图像检索算法

为了提高多特征融合图像检索的效果,本文提出了一种基于分块颜色直方图和GWLBP的图像检索算法。算法采用K-means均值聚类对RGB颜色空间进行颜色聚类,再将4×4均匀分块图像分成9个子块,提取每个子块的颜色体积直方图,并赋予不同权值计算颜色特征;利用Gabor滤波器组对输入图像进行不同分辨率和方向滤波,然后将不同方向上局部滤波器输出结果与全局滤波器输出结果的平均值进行比较,并进行二值化,据此提出3种不同的GWLBP算子来提取纹理特征。最后对图像的颜色和纹理特征高斯归一化,采用加权平均来融合颜色和纹理的特征距离。通过实验仿真可知,与其他3种算法相比,本算法对正常和有旋转倾向的图像都有较高的查全率和查准率。     

基于YCbCr高阶统计量的图像隐写分析

论文提出了一种应用小波变换基于YCbCr图层高阶统计量的JPEG图像通用隐写分析算法,该算法不局限于某种特定的隐匿算法。由于JPEG图片使用的是YCbCr颜色模型,因而若仍在RGB色彩层上构造高阶统计量,必然会淡化隐写后图像的特征,所以本文提出在YCbCr色彩层上处理高阶统计量,消除了RGB色彩模型同YCbCr色彩模型之间转化所产生的误差;统计量的构造通过原图与切割图像的差异,可减少SVM训练的样本数。