数字多媒体取证技术综述

数字多媒体取证是信息安全一个刚刚兴起的研究领域,研究数字多媒体取证技术对确保多媒体数据的可靠性有着极其重要的意义。以数字图像取证为代表,从篡改检测、来源辨识、真实性鉴定、设备成分取证以及多媒体取证方法的可靠性等五个方面对现有数字多媒体取证技术进行综述,重点介绍了典型算法,并指出当前研究中存在的一些主要问题,给出本领域未来的研究方向。     

Using decision fusion of feature selection in digital forensics for camera source model identification

Digital forensics, which identifies the characteristics and origin of a digital device, has become a new field of research. If digital content will serve as evidence in court, similar to its non-digital counterparts, digital forensics can play a crucial role in identifying the source model or device. To achieve this goal, the relationship between an image and its camera model will be explored. Various image-related and hardware-related features are utilized in the proposed model by a support vector machine approach along with decision fusion techniques. Furthermore, the optimum feature subset to achieve the highest accuracy rate is also explored.     

数字音频来源被动取证研究综述

数字音频来源被动取证研究旨在不依赖主动嵌入的数字水印或数字签名等冗余信息,通过原始数字音频数据的内在设备信息提取出表征设备源机器指纹的特征,进而对数字音频证据来源做出判断,在司法取证、军事信息、新闻传播等领域有着广泛的应用前景。目前,数字音频来源被动取证的研究综述面临时效性不足、针对性不够的问题。据此,给出了数字音频来源被动取证的研究框架和基本思路。对该领域常用的数据集做了简要的分析。根据数字音频来源被动取证的研究对象,将领域内的研究分为特征表达和表征建模两大模块,对频域信息特征、倒谱特征、基于高斯超矢量信息的特征、融合特征、深度特征五类特征,高斯混合取证模型、支持向量机决策模型、稀疏表达分类器决策模型、其他机器学习决策模型、深度学习决策模型五类模型的性能进行了比较分析。总结分析了数字音频来源被动取证领域的研究现状和存在的问题,并对未来的研究方向进行了展望。     

基于非抽样Contourlet变换的图像模糊取证

数字图像被动盲取证技术是对图像的完整性和真实性进行鉴别.图像遭受篡改操作后,为了消除图像伪造在拼接边缘产生畸变,伪造者通常会采用后处理消除伪造痕迹,其中,模糊操作是最常用的手法之一.因此提出了一种针对人工模糊的取证方法.首先,利用非抽样Contourlet变换分析图像边缘点特征进行边缘点分类;然后通过统计正常边缘点与模糊边缘点之间的差异鉴别模糊边缘;最后引入局部清晰度来区分人工模糊与离焦模糊,从而最终标定人工篡改边缘痕迹.实验表明该方法能够有效地检测出图像人工模糊篡改操作,较为准确地定位图像篡改边界.伪造图像边缘模糊越严重方法的检测效果越好.与其他模糊检测方法相比所提方法具有像素级别定位能力.     

Region duplication detection based on hybrid feature and evaluative clustering

Multimedia Tools and Applications - Digital image are easy to be tampered by the photo editing software. Therefore, digital image forensics which aims at validating the authenticity of the digital...     

数字取证技术及其发展趋势

数字取证技术已经成为信息安全领域研究热点之一。本文首先分析了数字取证的定义以及数字证据的特性。然后从取证过程模型、取证分析技术以及取证产品、标准和法规方面重点阐述了数字取证技术的研究现状,讨论了数字取证分析技术的分类方法以及文件雕刻取证分析技术。分析了数字取证领域中存在的难点问题,探讨了数字取证技术研究的发展趋势。     

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.     

基于隐私保护的数字图像取证外包技术框架研究

将数字取证服务外包给云有利于满足公众和各类机构的鉴别服务需求,也是数字取证技术发展的必然。然而这也带来了隐私泄露的风险。在已有的相关领域的研究基础上,给出了具有隐私保护能力的数字图像取证云平台的系统框架,并对Copy-move检测、图像来源鉴别和基于自然图像统计特性的篡改检测3种数字图像取证技术给出了隐私保护实现框架,进而探讨了隐私保护的数字图像取证可能的发展方向。     

一种针对手写体图像的取证算法

数字图像取证是信息安全领域的研究热点.在手写体图像方面,现有的取证算法对于图像采用各种重采样技术的篡改,其检测效果并不理想.在本文中,我们根据源区域和篡改区域的特征值不变性,提出一种简单有效的盲取证算法.该算法实现了手写体图像篡改的自动检测和篡改区域的定位,并且比现有的重采样检测方法对相同的手写体图像具有更好的检测率,尤其是在图像中的字有模糊、缺损的情况下检测的优势更明显.实验结果表明,对于经过各种重采样处理的手写体图像,该算法比现有取证算法的检测率高20%,当虚警率小于1%时,本算法的检测率达96.9%以上.     

一种DCT变换域的图象数字水印技术

图象数字水印技术是一种在数字图象中嵌入数字信息,从而保护图象版权的方法。介绍了一种离散余弦变换（DCT）域的图象水印方法,嵌入水印信息时考虑了人眼的视觉特性,具有较强的稳健性、视觉透明性。水印的检测采用统计的方法,不依赖原宿主图象。     

数字图像复制粘贴篡改取证技术比较研究

本文介绍了目前几种对数字图像复制粘贴篡改取证的算法,比较了各种算法的性能和特点,分析和讨论复制粘贴篡改的检测原理,并展望了该技术未来的发展方向。     

面向GAN生成图像的被动取证及反取证技术综述

生成对抗网络（generative adversarial network,GAN）快速发展,并在图像生成和图像编辑技术等多个方面取得成功应用。然而,若将上述技术用于伪造身份或制作虚假新闻,则会造成严重的安全隐患。多媒体取证领域的研究者面向GAN生成图像已提出了多种被动取证与反取证方法,但现阶段缺乏相关系统性综述。针对上述问题,本文首先阐述本领域的研究背景和研究意义,然后分析自然图像采集与GAN图像生成过程的区别。根据上述理论基础,详细介绍了现有GAN生成图像的被动取证技术,包括：GAN生成图像检测算法,GAN模型溯源算法和其他相关取证问题。此外,针对不同应用场景介绍基于GAN的反取证技术。最后,通过实验分析当前GAN生成图像被动取证技术所面临的挑战。本文根据对现有技术从理论和实验两方面的分析得到以下结论：现阶段,GAN生成图像的被动取证技术已在空间域和频率域形成了不同技术路线,较好地解决了简单场景下的相关取证问题。针对常见取证痕迹,基于GAN的反取证技术已能够进行有效隐藏。然而,该领域研究仍存在诸多局限：1）取证与反取证技术的可解释性不足;2）取证技术鲁棒性和泛化性较弱;3）反取证技术缺乏多特征域协同的抗分析能力等。上述问题和挑战还需要研究人员继续深入探索。     

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.     

重采样图像的盲检测技术

数字图像取证技术是一门不依赖任何图像先验知识而对图像来源和内容真实性进行认证的新兴技术。重采样检测是数字图像取证的一个重要分支,已成为当前的研究热点。详细分析对比了两类图像重采样盲检测算法：基于期望值最大化的重采样检测和基于差分周期特性的重采样检测,并对当前应用和后续研究进行相关论述。     

State of the art in passive digital image forgery detection: copy-move image forgery

Authenticating digital images is increasingly becoming important because digital images carry important information and due to their use in different areas such as courts of law as essential pieces of evidence. Nowadays, authenticating digital images is difficult because manipulating them has become easy as a result of powerful image processing software and human knowledge. The importance and relevance of digital image forensics has attracted various researchers to establish different techniques for detection in image forensics. The core category of image forensics is passive image forgery detection. One of the most important passive forgeries that affect the originality of the image is copy-move digital image forgery, which involves copying one part of the image onto another area of the same image. Various methods have been proposed to detect copy-move forgery that uses different types of transformations. The goal of this paper is to determine which copy-move forgery detection methods are best for different image attributes such as JPEG compression, scaling, rotation. The advantages and drawbacks of each method are also highlighted. Thus, the current state-of-the-art image forgery detection techniques are discussed along with their advantages and drawbacks.     

基于视觉特性的小波域数字图像水印算法

在信息时代,数字图像成为人们储存信息、传播知识和分析问题的重要媒体。但是,随着网络技术的发展和缺 乏相应的监督技术,在网络中传播的数字图像极易被恶意盗用和非法篡改。在缺乏技术保障的情况下,图像的拥有者很难维 护自己的所有权。为了解决这一问题,数字水印技术应运而生。本文提出了一种在小波域内综合考虑人的视觉特性和鲁棒性 的水印算法,该算法简单方便、效果好。     

数字图像空域加密技术综述

数字图像包含了大量可视化的隐私信息,其在公共信道的安全传输和云环境下的可信存储难以得到充分的保障。数字图像加密技术作为一种重要的隐私保护手段被广泛应用于各个领域。数字图像空域加密指将图像看作二维矩阵,从灰度值和像素坐标两个方面对图像进行可逆变换。数字图像的空域加密结构包括置乱与扩散。置乱是指通过改变像素的坐标实现对像素位置信息的隐藏;扩散是指建立密文图像与明文图像之间的强关联,保证加密算法的错误传播无界特性。该文详细介绍了现阶段4类主流的数字图像空域加密方法,对比分析不同空域加密的优缺点,给出7种常用的数字图像加密技术安全性的评估方法,并指出未来可能的数字图像加密技术的研究方向,提供数字图像加密技术的整体概述。     

利用块效应特征的JPEG图像盲取证研究进展

数字图像的盲取证技术由于不依赖任何预嵌入的信息来鉴别图像真实性和完整性的优势,正逐步成为数字媒体安全领域新的研究热点。由于JPEG图像是目前最流行的图像格式,并且块效应是JPEG图像与生俱来的本质特征,因此如何更加有效地利用块效应特征对JPEG图像的真伪进行盲取证研究具有非常重要的现实意义和应用价值。首先对目前国内外利用JPEG图像编码特性的盲取证方法进行归类分析;然后重点针对利用块效应特征的JPEG图像盲取证技术展开讨论,详细介绍并总结了基于块效应测度和基于块效应网格提取的两类盲取证算法的核心思想和局限性;最后提出了存在的问题及未来的研究方向。     

Video content authentication techniques: a comprehensive survey

In this digital day and age, we are becoming increasingly dependent on multimedia content, especially digital images and videos, to provide a reliable proof of occurrence of events. However, the availability of several sophisticated yet easy-to-use content editing software has led to great concern regarding the trustworthiness of such content. Consequently, over the past few years, visual media forensics has emerged as an indispensable research field, which basically deals with development of tools and techniques that help determine whether or not the digital content under consideration is authentic, i.e., an actual, unaltered representation of reality. Over the last two decades, this research field has demonstrated tremendous growth and innovation. This paper presents a comprehensive and scrutinizing bibliography addressing the published literature in the field of passive-blind video content authentication, with primary focus on forgery/tamper detection, video re-capture and phylogeny detection, and video anti-forensics and counter anti-forensics. Moreover, the paper intimately analyzes the research gaps found in the literature, provides worthy insight into the areas, where the contemporary research is lacking, and suggests certain courses of action that could assist developers and future researchers explore new avenues in the domain of video forensics. Our objective is to provide an overview suitable for both the researchers and practitioners already working in the field of digital video forensics, and for those researchers and general enthusiasts who are new to this field and are not yet completely equipped to assimilate the detailed and complicated technical aspects of video forensics.     

基于人类视觉系统的水印容量分析

数字水印容量是指在满足不可感知性和鲁棒性的前提下所能隐藏信息的数量.旨在人类视觉系统的特性的基础上,分析水印的容量.先对图像进行离散余弦变换,然后由Watson的视觉掩蔽模型计算每个系数的临界可见误差,即最大可修改幅度,在这里对这个最大幅度值进行了调整,最后利用多比特估计方法得到了水印容量的计算公式.随后对标准图像进行了实验测试,得出了调整因子与容量、峰值信噪比和平均嵌入量的关系.只要确定了峰值信噪比,调整因子和水印容量就确定了.