数字图像取证的被动技术综述

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"这篇研究论文是对被动式数字图像取证技术的调查，由LUO Weiqi, QU Zhenua, PAN Feng, 和HUANG Jiwu合作完成，发表在Frontiers of Computer Science in China, 2007年，第1卷第2期，页码166−179。DOI：10.1007/s11704-007-0017-0。" 随着数字图像在互联网和其他应用中的广泛应用，图像处理技术快速发展，篡改数字图像变得越来越容易且不留明显痕迹。这引发了一系列问题，如图像认证。过去几年，一种新的被动式图像取证技术迅速发展，它与基于签名或水印的方法不同，不需要预先生成的签名或嵌入的水印。这种技术假设不同的成像设备或处理过程会在输出图像中引入不同的固有模式，这些潜在模式是可追踪的，并可用于识别图像的真实性。数字图像取证的主要目标是检测和定位图像的篡改，以及确定其来源。被动技术主要依赖于分析图像中的元数据、噪声模式、压缩特征、传感器痕迹等。例如，相机传感器的固有噪声模式（如光敏像素阵列的噪声分布）可以作为设备指纹，用于区分不同相机拍摄的图像。此外，图像的JPEG压缩痕迹也可以提供线索，因为多次压缩会累积特征，可以揭示图像的编辑历史。论文可能详细讨论了以下关键点： 1. 图像元数据分析：元数据包含有关图像捕获和处理的信息，如相机型号、拍摄时间、曝光设置等，它们可能在篡改过程中被修改或丢失，因此分析元数据可以帮助识别篡改。 2. 噪声模式：每台相机都有独特的噪声模式，源自传感器的物理特性。通过比较图像的噪声结构，可以识别图像是否被从其他源拼接或替换。 3. 压缩特征分析：JPEG压缩会产生特定的量化和熵编码痕迹。通过比较这些特征，可以发现图像是否经过篡改或重新压缩。 4. 传感器指纹：不同相机的传感器会有微小差异，形成独特的“指纹”。这些指纹可以在图像的边缘和颜色过渡区域找到，用于辨识图像的原始设备。 5. 图像处理痕迹：图像编辑软件通常会在图像上留下痕迹，如色彩校正、锐化或模糊操作。通过对这些处理的识别，可以推断图像的编辑历史。这篇综述文章很可能深入探讨了这些技术的原理、实施方法、优势和局限性，同时提供了对现有技术的综合评估，并可能展望了未来的研究方向。这种被动式技术的发展对于维护网络安全、打击虚假信息传播以及保护知识产权等方面具有重要意义。

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is noted that some of the operations involve malicious tam-

pering, while some do not change the contents of the image,

e.g., color and contrast adjustment, etc. However, some op-

erations may be confusing. Take double JPEG compression,

for example. Double JPEG-compressed images often result

from forgeries when a part of the original image is replaced

by another part from the same (region-duplication) or an-

other (splicing) image and then resaved. On the other hand,

a user may resave a high-quality JPEG image to a lower-

quality one to save storage space. Therefore, detection of

image alteration does not necessarily prove malicious tam-

pering in some situations. But it can make us doubt about

the contents of the image and help us with further analysis.

Figure 4 shows the generic image alteration detection

scheme. The test image X may be an original image, or may

be altered by a certain operation. Similarly as shown in Fig.3,

we first extract the features from X and obtain the origi-

nal/altered patterns mainly using the knowledge of the im-

age manipulation model, or sometimes combining with the

statistical characteristic in nature scenes, and the image ac-

quisition model, etc. Then we compare the distance between

the features and the patterns to decide whether or not image

X has been altered.

Fig. 4 Generic image alteration detection

In brief, comparing with signature-based and watermark-

based methods, passive technology need not use any extra

embedded information in advance. We can identify the

source or detect the alteration of the image just by using the

patterns of the digital images themselves. From another

viewpoint, the images have some inherent “watermarks”

(patterns). The “watermarks” are introduced not by our in-

trusive operations, but by the various imaging devices or

constrained by some statistical characteristics in the nature

scenes. The “watermarks” would be altered after some

post-processing. These different “watermarks” can be used

as evidence for image forensics.

In most cases, passive forensics can be converted to a

problem of pattern recognition. The solution to the problem

is finding the different patterns according to the knowledge

from various imaging devices or the manipulations or the

nature scene constraints, etc. The selected patterns with

distinguishing ability are crucial for this new technology.

In the following section, we will give a detailed overview

of some prior typical works in this new area, and show

which exact inherent patterns can be applied to digital im-

age forensics.

3 Techniques of passive forensics

As mentioned above, image-source identification and im-

age-alternation detection are the two main issues in passive

forensics technology. In this section, we will discuss these

two issues.

3.1 Identification of image source

Digital images can be captured by various imaging devices

such as digital cameras, scanners, and so on. These imaging

devices are available to our average users. In many applica-

tions, we must deal with problems about the source of the

digital images. In a court case involving child pornography,

for example, if a suspect camera has been found, is it possi-

ble to determine whether the images have been captured by

the same camera? In other cases, images can also be gener-

ated entirely by computer. As computer graphics technology

progresses, it becomes difﬁcult to distinguish by sight the

photographic images from the computer-generated images.

Recently, some digital forgeries mixing the real scene and

computer-generated virtual scene together have been re-

ported. Is it possible to identify and distinguish these two

kinds of images?

3.1.1 Identiﬁcation of digital camera

High-resolution and low-cost digital cameras have been

rapidly replacing the typical ﬁlm cameras. Now, most im-

ages in our daily life are acquired by various brands of digi-

tal cameras, such as Canon, Nikon, Sony, Olympus, etc. One

of the main problems related to source identiﬁcation is the

classification of the different camera models or individuals

for a given image.

The most straightforward solution for camera identi fica-

tion is to check the EXIF (Exchangeable Image File, most

digital cameras now use the EXIF format [8]) header of the

output image (Raw, Tiff or JPEG format ﬁle, and most digi-

tal cameras export images in the JPEG format). Some set-

tings of an image are stored in the headers, and the settings

are constrained by a given camera, such as the manufacturer,

the model of the camera, image size, exposure time, and the

quantization matrix used in JPEG compression [9], etc. If

the given image settings are out of the range of the given

camera, it can be concluded that the image did not come

from the camera or it was not the original one at least.

However, we cannot distinguish among the cameras of the

same or similar model whose images contain the same

header information. Furthermore, the header information can

be easily replaced or made consistent by JPEG recompres-

sion or other operations. Can we find other features that

cannot be easily removed by average users? In the following,

we describe brieﬂy the operations inside a typical digital

camera first, and then analyze which knowledge can be used

as reliable evidence for camera identiﬁcation.

Figure 5 shows the operations inside a typical digital

camera. The light coming from the scene passes through the

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数字图像取证的被动技术综述

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