基于图像内容和噪音特征的扫描仪源辨识

以扫描仪获得的数字扫描图像为研究对象,提出一种基于图像内容和噪音特征的扫描仪源辨识方法,提取数字扫描图像中的颜色特征、质量特征和邻或预测特征,生成一个72维的特征向量以辨识扫描仪的来源,并借助支持向量机确定扫描仪的品牌或型号.实验结果表明,该方法具有较高的分类精度,并且在数字扫描图像被压缩或剪切的情况下均具有较好的鲁棒...     

Detection of JPEG double compression and identification of smartphone image source and post-capture manipulation

Digital multimedia forensics is an emerging field that has important applications in law enforcement and protection of public safety and national security. In digital imaging, JPEG is the most popular lossy compression standard and JPEG images are ubiquitous. Today’s digital techniques make it easy to tamper JPEG images without leaving any visible clues. Furthermore, most image tampering involves JPEG double compression, it heightens the need for accurate analysis of JPEG double compression in image forensics. In this paper, to improve the detection of JPEG double compression, we transplant the neighboring joint density features, which were designed for JPEG steganalysis, and merge the joint density features with marginal density features in DCT domain as the detector for learning classifiers. Experimental results indicate that the proposed method improves the detection performance. We also study the relationship among compression factor, image complexity, and detection accuracy, which has not been comprehensively analyzed before. The results show that a complete evaluation of the detection performance of different algorithms should necessarily include image complexity as well as the double compression quality factor. In addition to JPEG double compression, the identification of image capture source is an interesting topic in image forensics. Mobile handsets are widely used for spontaneous photo capture because they are typically carried by their users at all times. In the imaging device market, smartphone adoption is currently exploding and megapixel smartphones pose a threat to the traditional digital cameras. While smartphone images are widely disseminated, the manipulation of images is also easily performed with various photo editing tools. Accordingly, the authentication of smartphone images and the identification of post-capture manipulation are of significant interest in digital forensics. Following the success of our previous work in JPEG double compression detection, we conducted a study to identify smartphone source and post-capture manipulation by utilizing marginal density and neighboring joint density features together. Experimental results show that our method is highly promising for identifying both smartphone source and manipulations. Finally, our study also indicates that applying unsupervised clustering and supervised classification together leads to improvement in identifying smartphone sources and manipulations and thus provides a means to address the complexity issue of the intentional post-capture manipulation on smartphone images.     

利用传感器噪声对复制遮盖篡改的检测

由于现今功能强大的图像编辑软件很容易就可以得到,所以对数字图像进行操作和编辑变得非常的容易,在一幅图像中添加或移掉一个重要的人或物并且不留任何痕迹是很有可能的.如果这些篡改图用在媒体或法律上,对社会将造成很大的影响.随着数码相机和摄像机的不断普及,验证数码图像变得越来越重要了.利用相机的传感器噪声对复制遮盖篡改图像进行检测,并根据其模板噪声进一步确定篡改区域.对多幅图像进行操作,实验证明效果不错.     

基于模式噪声大分量的手机相机来源检测

将模式噪声作为固定指纹特征应用于手机相机来源检测时,存在计算复杂、效率不高等问题。为此,提出一种基于模式噪声大分量信息的手机相机来源检测方法。利用光响应敏感点的成像特性及对模式噪声的影响,将其从模式噪声中分离出来,构成模式噪声的大分量信息,并作为新的模式噪声进行手机相机来源检测。实验结果表明,与传统基于模式噪声的图像来源检测方法相比,该方法不仅能有效辨识手机相机图像的来源,而且能减少检测计算量。     

基于谱相关性的数字图像真伪鉴别

如何区分逼真的计算机生成图像和真实的自然图像,是数字图像取证领域的一个重要研究方向。提出了一种基于谱间相关性的图像真伪鉴别算法。在单CCD数码成像过程中,每个像素只采集单一颜色值,缺失的颜色值通过颜色滤波阵列插值获得,而基于颜色滤波阵列插值的去马赛克方法会引起彩色图像三个颜色分量之间较高的谱间相关性。算法利用小波变换和标准互相关系数提取颜色组件谱间相关性,作为区分和识别特征。通过在标准图像库上的实验测试,表明所提取特征有效捕获了二类图像的差别,并具有较高的检测率。     

Efficient illumination compensation techniques for text images

With the great advantages of digitization, more and more documents are being transformed into digital representations. Most content digitization of documents is performed by scanners or digital cameras. However, the transformation might degrade the image quality caused by lighting variations, i.e. uneven illumination distribution. In this paper we describe a new approach for text images to compensate uneven illumination distribution with a high degree of text recognition. Our proposed scheme is implemented by enhancing the contrast of the scanned documents, and then generating an edge map from the contrast-enhanced image for locating text area. With the information of the text location, a light distribution image (background) is created to assist the producing of the final light balanced image. Simulation results demonstrate that our approach is superior to the previous works of Hsia et al. (2005, 2006).     

Automatic source scanner identification using 1D convolutional neural network

In this digital world, digitized documents can be considered original or a piece of evidence; checking the authenticity of any suspicious image has become an unavoidable concern to preserve the trust in its legitimacy. However, identifying the source of a digital image without any prior embedded information is a very challenging task. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN) model to solve the source scanner identification (SSI) problem blindly. Unlike traditional methods based on handcrafted features, the proposed framework can dynamically learn and extract scanner device-specific features. This work, comprised of the 1D-CNN and a support vector machine (SVM) as a classifier, was trained on nine scanners of different brands and models. The experimental result shows that our model achieves 98.15% accuracy on full images and overall accuracy of 93.13% on segments from test images, outperforming other state-of-art approaches. Our model also proves to be able to distinguish between scanners of the same model. Furthermore, the SVM classifier improved the 1D-CNN accuracy by approximately 3% compared to its original configuration.

     

基于模式分类的图像区域作伪检测

为了鉴别一幅数字图像是否存在作伪的区域,提出一种利用改进的图像特征进行区域作伪检测的算法.基于模式分类的思想,该方法把图像分割成适当大小的块,从图像块中提取特征数据,用SVM分类器训练数据并得到支持向量机模型,利用该模型检测嫌疑图片是否存在作伪.该算法从噪声相关性、残差噪声、图像质量、小波域等方面分析相机图片的特点,获取每种的统计特征,形成特征集.实验结果表明,该方法能有效地检测出图像的具体作伪区域.     

Determining Image Origin and Integrity Using Sensor Noise

In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.     

Digital camera identification from sensor pattern noise

In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's pattern noise. For each camera under investigation, we first determine its reference pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using a denoising filter. To identify the camera from a given image, we consider the reference pattern noise as a spread-spectrum watermark, whose presence in the image is established by using a correlation detector. Experiments on approximately 320 images taken with nine consumer digital cameras are used to estimate false alarm rates and false rejection rates. Additionally, we study how the error rates change with common image processing, such as JPEG compression or gamma correction.     

基于数码相机固有特性的篡改检测

由于现今功能强大的图像编辑软件很容易就可以得到，所以对数字图像进行操作和编辑变得非常的容易，在一幅图像中添加或移掉一个重要的人或物并且不留任何痕迹是很有可能的。如果这些篡改图像用在媒体或法律上，对社会将造成很大的影响。随着数码相机和摄像机的不断普及，验证数字图像变得越来越重要了。文章利用数码相机的两个固有特性对图像进行篡改检测，对多幅图像进行操作，实验证明有不错的检测效果。     

All-in-focus imaging using average filter-based relative focus measure

Digital images are normally taken by focusing on an object, resulting in defocused background regions. A popular approach to produce an all-in-focus image without defocused regions is to capture several input images at varying focus settings, and then fuse them into an image using offline image processing software. This paper describes an all-in-focus imaging method that can operate on digital cameras. The proposed method consists of an automatic focus-bracketing algorithm that determines at which focuses to capture images and an image-fusion algorithm that computes a high-quality all-in-focus image. While most previous methods use the focus measure calculated independently for each input image, the proposed method calculates the relative focus measure between a pair of input images. We note that a well-focused region in an image shows better contrast, sharpness, and details than the corresponding region that is defocused in another image. Based on the observation that the average filtered version of a well-focused region in an image shows a higher correlation to the corresponding defocused region in another image than the original well-focused version, a new focus measure is proposed. Experimental results of various sample image sequences show the superiority of the proposed measure in terms of both objective and subjective evaluation and the proposed method allows the user to capture all-in-focus images directly on their digital camera without using offline image processing software.     

Efficient noise reduction in images using directional modified sigma filter

A noise reduction of images using a directional modified sigma filter is proposed. It is important that an image should include accurate values without noise for large-scale data processing of a cloud computing environment. A conventional sigma filter has been shown to be a good solution both in terms of filtering accuracy and computational complexity. However, the sigma filter does not preserve small edges well especially for the high level of additive noise. In this paper, we propose a new method using a modified sigma filter. In our proposed method, an input image is first decomposed into two components that have features of horizontal, vertical, and diagonal direction. Then two components are applied: high-pass filtering (HPF) and low-pass filtering (LPF). By applying the conventional sigma filter separately on each of them, an output image is reconstructed from the filtered components. Added noise is removed and our proposed method preserves the edges in the image. Comparative results from experiments show that the proposed algorithm achieves higher gains than the sigma filter and modified sigma filter, which are 2.6 dB PSNR on average and 0.5 dB PSNR, respectively. When relatively high levels of noise are added, the proposed algorithm shows better performance than the two conventional filters. The proposed method can be efficiently applied in digital cameras, digital TV, and smart phones.     

Color laser printer forensic based on noisy feature and support vector machine classifier

Digital forensics in the ubiquitous era can enhance and protect the reliability of multimedia content where this content is accessed, manipulated, and distributed using high quality computer devices. Color laser printer forensics is a kind of digital forensics which identifies the printing source of color printed materials such as fine arts, money, and document and helps to catch a criminal. This paper present a new color laser printer forensic algorithm based on noisy texture analysis and support vector machine classifier that can detect which color laser printer was used to print the unknown images. Since each printer vender uses their own printing process, printed documents from different venders have a little invisible difference looks like noise. In our identification scheme, the invisible noises are estimated with the wiener-filter and the 2D Discrete Wavelet Transform (DWT) filter. Then, a gray level co-occurrence matrix (GLCM) is calculated to analyze the texture of the noise. From the GLCM, 384 statistical features are extracted and applied to train and test the support vector machine classifier for identifying the color laser printers. In the experiment, a total of 4,800 images from 8 color laser printer models were used, where half of the image is for training and the other half is for classification. Results prove that the presented algorithm performs well by achieving 99.3%, 97.4% and 88.7% accuracy for the brand, toner and model identification respectively.     

面向真实性鉴别的数字图像盲取证技术综述

数字图像盲取证技术作为一种不依赖任何预签名提取或预嵌入信息来鉴别图像真伪和来源的技术, 正逐步成为多媒体安全领域新的研究热点, 且有着广泛的应用前景. 首先简要描述了图像盲取证技术要解决的问题和任务. 根据图像鉴别使用的取证特征, 将用于真实性鉴别的图像盲取证技术划分为三类: 基于图像伪造过程遗留痕迹的盲取证技术、基于成像设备一致性的盲取证技术和基于自然图像统计特性的盲取证技术, 然后分别阐述了这三类取证技术的基本特征和典型方法, 对不同算法进行了性能比较和总结. 最后综合近年来国内外学者在面向真实性鉴别的图像盲取证技术方面的主要研究成果, 探讨了图像盲取证技术存在的问题及未来研究方向.     

Effective demosaicking algorithm based on edge property for color filter arrays

Under the influence of digitization, digital still cameras (DSC) are becoming prevalent in recent years. The image capturing and file storing process of a digital still camera involves multiple image processing and precise corrective calculations. In order to reduce cost and volume, a digital still camera usually utilizes only one sensor and color filter array (CFA) for capturing images, and reconstructs a corresponding full-color image using a color interpolation method. Demosaicking is the first step of image processing of digital still cameras and has been integrated into the design of a variety of digital still cameras. If noise and blurred edges exist from the onset of image reconstruction, a post-processing can do little to improve the quality of the reconstructed image. A demosaicking method is proposed in this paper to prevent the occurrence of color artifacts. By detecting the edge characteristics of a digital image, accurate weights can be obtained for image interpolation, before refinement is made in post-processing. After comparing the experiment results of this paper with those of previously proposed methods, it is found that the proposed method can effectively reduce color artifacts and enhance image quality.     

Digital orthophotography: mapping with pictures

Aerial photographs and maps would seem a natural combination to record and analyze geographical information: maps provide geometric information and photographs add realistic, timely detail. But cameras record images on a flat plane, whereas the earth is curved and its terrain takes on many varied shapes-all of which distort the image geometry and render it invalid for mapping and geographic analysis. Digital orthophotographs solve this problem. A digital orthophoto starts with a rasterized (scanned) aerial photograph; a process called rectification (described below) removes distortions arising from the camera lens, the aircraft's position, and elevation and other topographical features. This transforms aerial photos into high-resolution digital images that correctly represent the geometry of an area and its terrain. These images can be used as standard true-scale representations of geographic sectors-a function already served by analog orthophotos, which must be painstakingly scanned and rectified in small strips or patches. Their fully digital format makes digital orthophotos useful as base maps in geographical information systems (GIS) used for creating and revising topographic and planimetric maps, vegetation and timber management, environmental impact assessments, and infrastructure assessment     

A body-mounted camera system for head-pose estimation and user-view image synthesis

In this paper, we propose a body-mounted system to capture user experience as audio/visual information. The proposed system consists of two cameras (head-detection and wide angle) and a microphone. The head-detection camera captures user head motions, while the wide angle color camera captures user frontal view images. An image region approximately corresponding to user view is then synthesized from the wide angle image based on estimated human head motions. The synthesized image and head-motion data are stored in a storage device with audio data. This system overcomes the disadvantages of head-mounted cameras in terms of ease of putting on/taking off the device. It also has less obtrusive visual impact on third persons. Using the proposed system, we can simultaneously record audio data, images in the user field of view, and head gestures (nodding, shaking, etc.) simultaneously. These data contain significant information for recording/analyzing human activities and can be used in wider application domains such as a digital diary or interaction analysis. Experimental results demonstrate the effectiveness of the proposed system.     

Digital Image Forensics via Intrinsic Fingerprints

Digital imaging has experienced tremendous growth in recent decades, and digital camera images have been used in a growing number of applications. With such increasing popularity and the availability of low-cost image editing software, the integrity of digital image content can no longer be taken for granted. This paper introduces a new methodology for the forensic analysis of digital camera images. The proposed method is based on the observation that many processing operations, both inside and outside acquisition devices, leave distinct intrinsic traces on digital images, and these intrinsic fingerprints can be identified and employed to verify the integrity of digital data. The intrinsic fingerprints of the various in-camera processing operations can be estimated through a detailed imaging model and its component analysis. Further processing applied to the camera captured image is modelled as a manipulation filter, for which a blind deconvolution technique is applied to obtain a linear time-invariant approximation and to estimate the intrinsic fingerprints associated with these postcamera operations. The absence of camera-imposed fingerprints from a test image indicates that the test image is not a camera output and is possibly generated by other image production processes. Any change or inconsistencies among the estimated camera-imposed fingerprints, or the presence of new types of fingerprints suggest that the image has undergone some kind of processing after the initial capture, such as tampering or steganographic embedding. Through analysis and extensive experimental studies, this paper demonstrates the effectiveness of the proposed framework for nonintrusive digital image forensics.     

Hole filling of triangular mesh segments using systematic grey prediction

Recent advances in reverse engineering technology have made it possible to acquire accurate point information about model surfaces using 3D laser scanners. However, data capture can be reduced by limitations in the scanner structure or characteristics of the scanned objects, resulting in holes in the triangular mesh model and reducing the model’s quality. The current study seeks to fill these holes using software-based calculations and systematic grey prediction, thus preserving the original exterior surface of the reconstructed model. A 3D digital dental model was acquired using a 3D laser scanner, followed by hole detection and surface mending. Due to the simplicity and minimal dataset used in grey theory, a grey prediction system was built to perform prediction-adjustment to preserve the original dental exterior surface.