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纸质出版日期： 2021-06-16 ，
录用日期： 2021-03-26
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李晓龙, 俞能海, 张新鹏, 张卫明, 李斌, 卢伟, 王伟, 刘晓龙. 数字媒体取证技术综述[J]. 中国图象图形学报, 2021,26(6):1216-1226.
Xiaolong Li, Nenghai Yu, Xinpeng Zhang, Weiming Zhang, Bin Li, Wei Lu, Wei Wang, Xiaolong Liu. Overview of digital media forensics technology[J]. Journal of Image and Graphics, 2021,26(6):1216-1226.
面对每天有数以百万计通过网络传播的多媒体数据，到底哪些内容是真实可信的，虚假内容的背后又经历了哪些篡改？数字取证技术将给出答案。该技术不预先嵌入水印，而是直接分析多媒体数据的内容，达到辨别真实性的目的。任何篡改和伪造都会在一定程度上破坏原始多媒体数据本身固有特征的完整性，由于其具有一致性和独特性，可作为自身的“固有指纹”，用于鉴别篡改文件。随着篡改媒体的数量与日俱增，社会稳定甚至国家安全受到了严重威胁。特别地，随着深度学习技术的快速发展，虚假媒体与真实媒体之间的感官差距越来越小，这对媒体取证研究提出了巨大挑战，并使得多媒体取证成为信息安全领域一个重要的研究方向。因此，目前迫切需要能够检测虚假多媒体内容和避免危险虚假信息传播的技术和工具。本文旨在对过去多媒体取证领域所提出的优秀检测取证算法进行总结。除了回顾传统的媒体取证方法，还将介绍基于深度学习的方法。本文针对当今主流的多媒体篡改对象：图像、视频和语音分别进行总结，并针对每种媒体形式，分别介绍传统篡改方法和基于AI（artificial intelligence）生成的篡改方法，并介绍了已公开的大规模数据集以及相关应用的情况，同时探讨了多媒体取证领域未来可能的发展方向。
Internet and social networks have become the main platforms for people to access and share various digital media. Among them
media based on images
videos
and audio carry more information and are the most eye-catching. With the rapid development of computer technology
image and video editing software and tools have appeared one after another
such as Photoshop
Adobe Premiere Pro
and VideoStudio. These editing software can be faster and easier to modify the media. The effect of image forgery is realistic
and the effect of video editing and synthesis is natural and smooth. In recent years
the image generation technology has also been greatly developed
and the visual effects of the generated images may be fake. The problem of multimedia forgery attracts people's attention. The purpose of forgery may be entertainment (such as beautifying images)
malicious modification of the content of images and videos (such as deliberately modifying photos of political figures or deliberately exaggerating the severity of news events)
and malicious copying. Image forgery incidents in recent years also remind people to focus on the security of media content. The authenticity of visual media content decreases and is increasingly being questioned. At present
millions of multimedia data are transmitted via the Internet every day. What type of content is true? What tampering was made behind the wrong content? The digital forensics technology proposed in recent years provides the answer. This technology does not embed a watermark in advance but directly analyzes the content of multimedia data to achieve the purpose of authenticity recognition. The basic principle is that the inherent characteristics of the original multimedia data are consistent and unique and can be used as its own "intrinsic fingerprint". Any tampering or forgery destroys its integrity to a certain extent. In recent years
media tampering has been increasing and has seriously threatened social stability and even national security. Especially with the rapid development of deep learning technology
the perceived gap between fake media and real media decreases. This finding poses a serious challenge to media forensic research and makes multimedia forensics an important issue in the field of information security research direction. Therefore
technologies and tools that can detect erroneous multimedia content are urgently required
and the spread of dangerous erroneous information is avoided. This article aims to summarize the excellent detection and forensics algorithms proposed in the previous multimedia forensics field. In addition to reviewing traditional media forensics methods
we introduce methods based on deep learning. This article summarizes the current mainstream multimedia tampering objects
namely
images
videos
and sounds. Each media form includes traditional tampering methods and artificial intelligence (AI)-based tampering methods. Among them
video tampering is mainly divided into intraframe tampering and interframe tampering. Intraframe tampering takes the video frame as a unit to delete objects on the screen or performing "copy and move" operations
and interframe tampering takes the video sequence as a unit to add or delete frames. Traditional methods for detecting fake videos can be divided into video encoding tracking detection
video content inconsistency detection
video frame repeated tampering
and copy and paste detection. AI-based error video detection technology focuses on detecting artifacts left over from the network generated in the imaging network
which is different from the imaging process of a real camera. The purpose of digital image forensics technology is to verify the integrity and authenticity of digital images. Image forensic methods can be divided into active methods and passive methods. Active image forensics includes embedding watermarks or signatures in digital images. The passive blind forensic (blind forensics) method is not limited by these factors. It distinguishes images by detecting traces of tampering in the image. Common image forgery and tampering include enhancement
modification
area duplication
splicing
and synthesis. The detection of partial replacement image is divided into the following: 1) Area copy and tamper detection
which copies and pastes part of the area in the image to other areas. During the copying process
the copied area may undergo various geometric transformations and postprocessing. 2) Image processing fingerprints detection. The visual difference caused by simple area copying
splicing
and tampering is still evident. The forger performs postprocessing
such as zooming
rotating
and blurring the image
to eliminate these traces. 3) In recompression fingerprint detection
tampered images inevitably undergo recompression; thus
digital image recompression detection can provide a powerful auxiliary basis for digital image forensics. For the traceability detection technology of forged images
most images are captured by the camera. The general physical structure of the camera and the physical differences between different cameras leave traces on the captured images. These traces (camera fingerprints) appear as a series of features on the image
and the acquisition device of this image can be identified by examining the fingerprint of the device embedded in the image. The detection technology for the overall image generated by AI also focuses on detecting the artifacts left by the network generated in the imaging network. In the previous decades
some digital audio forensic studies have focused on detecting various forms of audio tampering. These methods check the metadata of audio files. In addition
the publicly available large-scale data sets and related applications are introduced
and the possible future development directions of the multimedia forensic field is discussed.
多媒体取证多媒体溯源篡改检测篡改定位虚假人脸
multimedia forensicsmultimedia traceabilityforgery detectionforgery localizationfake face
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