数字图像操作取证技术研究
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摘要
随着数字媒体编辑技术的快速发展,数字图像修改或篡改变得非常容易,违规编辑和虚假图像也频繁出现,严重损害了数字媒体信息的可信性与安全性。在司法取证和新闻纪实等应用领域,常需要通过技术手段验证图像数据的原始性和真实性,并还原图像的操作历史以获取更多取证信息。因此,研究数字图像取证技术对于实现数字媒体内容认证具有重要意义和实用价值。本论文以数字图像操作取证技术为主要研究内容,取得创新性研究成果包括：
1)提出了两种基于边缘特征一致性分析的图像拼接检测方法。第一种方法利用自然边缘与拼接边界在CFA (Color Filter Array)内插特征一致性方面的差异,提出了一种快速有效的图像拼接检测算法,可有效克服现有大多数拼接检测技术需要统计学习导致算法复杂度较高的缺陷。第二种方法针对图像拼接后进行边界模糊润饰这一实际篡改情形,提出了一种基于边缘模糊度估计的拼接检测算法,可有效定位模糊润饰后的拼接边界。
2)提出了分别针对USM(Unsharp Masking)锐化和中值滤波的图像操作取证算法。在国际上较早提出了数字图像锐化和中值滤波取证问题。对图像USM锐化过程进行信号建模且给出了相应的数学描述,分析了过冲效应的产生机理,由此设计了有效的USM锐化检测方案。同时,从理论上分析了中值滤波所引起的图像一阶微分域统计特性异常,提出了一种快速有效的中值滤波操作检测算法。实验结果表明,此锐化和中值滤波取证算法均可有效鉴别相应的图像滤波操作。
3)提出了图像对比度增强取证系列算法。为有效检测中低等质量JPEG图像上的对比度增强操作,提出了一种基于灰度直方图峰谷形状分析的对比度增强检测方法；利用峰谷位置分布与像素值映射函数之间的对应关系设计了一种快速有效的伽玛参数盲估计方法；针对源图像区域经历不同的对比度增强情形,设计了一种有效的图像拼接检测方法；最后简要分析了现有对比度增强取证算法的安全性。实验结果表明,所提对比度增强取证相关算法均取得较高性能。
4)提出了一种半侵入式重采样算子源取证算法。从理论上推导出严格单调信号在经历传统型和几何抖动型重采样后一阶微分极性的变化规律,通过设计合适的模式图像,提出了一套完整的重采样算子源鉴别方法。实验结果表明,该方法既可识别重采样软件的内含算子,也可在特定情形下检测反取证型重采样操作。
With the rapid development of digital media editing techniques, digital image alter-ation and tampering become quite easy. The illegally manipulated and forged images emerge frequently. As such the credibility and security of digital media information are destroyed seriously. In the applications such as law enforcement and news recording, it is necessary to verify the originality and authenticity of digital images, and make clear the image manipulation history to get more information. Therefore, the research on dig-ital image manipulation forensics is significant and valuable for realizing the auth-entication of multimedia content. This thesis addresses the digital image manipulation forensics problem and gains the following novel research achievements:
1) Two splicing detection algorithms are proposed based on the consistency analysis of edge related features in digital images. First, the consistency between the CFA (Color Filter Array) interpolation artifacts in natural edges and those in splicing boundaries is exploited to design an image slicing detection algorithm. Comparing with the most prior methods based on statistical learning, our proposed method is cost-efficient and owns lower algorithm complexity. Second, another splicing detection algorithm is proposed based on the estimation of edge-based blurriness. Such an algorithm could be used in the practical scenario that post-burring is enforced onto the splicing boundary, which would be located accordingly.
2) Two forensic algorithms are proposed to detect the digital image USM (Unsharp Masking) sharpening and median filtering manipulations, respectively. The authors address the image sharpening and median filtering forensics problems earlier in the digital forensics community. The USM sharpening process is modeled in signal and described in mathematics. The mechanism of producing overshoot artifacts is analyzed. And consequently, an effective sharpenging detection scheme is designed. Meanwhile, the statistical abnormity in the image's first-order difference domain, which is incurred by median filtering, is analyzed theoretically. A fast and effective median filtering detection algorithm is proposed. Test results show the proposed sharpening and median filtering forensic algorithms could identify the corresponding manipulations efficiently.
3) A series of algorithms on image contrast enhancement forensics are proposed. To efficiently detect contrast enhancement in middle/low quality JPEG compressed images, a global contrast enhancement detection algorithm is proposed based on the shape an- alysis of the gray level histogram peak/gap bins. By means of the correspondence be tween the peak/gap bins position distribution and the involved pixel value mapping, a fast and efficient algorithm is proposed to estimate the gamma parameter blindly. As for the scenario that source regions suffer different contrast enhancement mappings, an effi-cient source-enhanced spliced image detection method is designed. Lastly, the security of existing contrast enhancement forensic algorithms is analyzed briefly. Test results demonstrate that our proposed contrast enhancement forensics methods could achieve good performance.
4) A semi non-intrusive forensic algorithm is proposed to identify the digital image resampling operator. As for the strictly monotone signal, its first derivative polarity regularity after suffering the traditional and geometric-dithering types of resampling is analyzed theoretically. Through designing suitable test pattern images, a unified resam-pling operator identification scheme is proposed. Test results show that the proposed method could identify the resampling operator embedded in softwares, as well as detect the anti-forensic type of resampling manipulation in some specific scenarios.
1)提出了两种基于边缘特征一致性分析的图像拼接检测方法。第一种方法利用自然边缘与拼接边界在CFA (Color Filter Array)内插特征一致性方面的差异,提出了一种快速有效的图像拼接检测算法,可有效克服现有大多数拼接检测技术需要统计学习导致算法复杂度较高的缺陷。第二种方法针对图像拼接后进行边界模糊润饰这一实际篡改情形,提出了一种基于边缘模糊度估计的拼接检测算法,可有效定位模糊润饰后的拼接边界。
2)提出了分别针对USM(Unsharp Masking)锐化和中值滤波的图像操作取证算法。在国际上较早提出了数字图像锐化和中值滤波取证问题。对图像USM锐化过程进行信号建模且给出了相应的数学描述,分析了过冲效应的产生机理,由此设计了有效的USM锐化检测方案。同时,从理论上分析了中值滤波所引起的图像一阶微分域统计特性异常,提出了一种快速有效的中值滤波操作检测算法。实验结果表明,此锐化和中值滤波取证算法均可有效鉴别相应的图像滤波操作。
3)提出了图像对比度增强取证系列算法。为有效检测中低等质量JPEG图像上的对比度增强操作,提出了一种基于灰度直方图峰谷形状分析的对比度增强检测方法；利用峰谷位置分布与像素值映射函数之间的对应关系设计了一种快速有效的伽玛参数盲估计方法；针对源图像区域经历不同的对比度增强情形,设计了一种有效的图像拼接检测方法；最后简要分析了现有对比度增强取证算法的安全性。实验结果表明,所提对比度增强取证相关算法均取得较高性能。
4)提出了一种半侵入式重采样算子源取证算法。从理论上推导出严格单调信号在经历传统型和几何抖动型重采样后一阶微分极性的变化规律,通过设计合适的模式图像,提出了一套完整的重采样算子源鉴别方法。实验结果表明,该方法既可识别重采样软件的内含算子,也可在特定情形下检测反取证型重采样操作。
With the rapid development of digital media editing techniques, digital image alter-ation and tampering become quite easy. The illegally manipulated and forged images emerge frequently. As such the credibility and security of digital media information are destroyed seriously. In the applications such as law enforcement and news recording, it is necessary to verify the originality and authenticity of digital images, and make clear the image manipulation history to get more information. Therefore, the research on dig-ital image manipulation forensics is significant and valuable for realizing the auth-entication of multimedia content. This thesis addresses the digital image manipulation forensics problem and gains the following novel research achievements:
1) Two splicing detection algorithms are proposed based on the consistency analysis of edge related features in digital images. First, the consistency between the CFA (Color Filter Array) interpolation artifacts in natural edges and those in splicing boundaries is exploited to design an image slicing detection algorithm. Comparing with the most prior methods based on statistical learning, our proposed method is cost-efficient and owns lower algorithm complexity. Second, another splicing detection algorithm is proposed based on the estimation of edge-based blurriness. Such an algorithm could be used in the practical scenario that post-burring is enforced onto the splicing boundary, which would be located accordingly.
2) Two forensic algorithms are proposed to detect the digital image USM (Unsharp Masking) sharpening and median filtering manipulations, respectively. The authors address the image sharpening and median filtering forensics problems earlier in the digital forensics community. The USM sharpening process is modeled in signal and described in mathematics. The mechanism of producing overshoot artifacts is analyzed. And consequently, an effective sharpenging detection scheme is designed. Meanwhile, the statistical abnormity in the image's first-order difference domain, which is incurred by median filtering, is analyzed theoretically. A fast and effective median filtering detection algorithm is proposed. Test results show the proposed sharpening and median filtering forensic algorithms could identify the corresponding manipulations efficiently.
3) A series of algorithms on image contrast enhancement forensics are proposed. To efficiently detect contrast enhancement in middle/low quality JPEG compressed images, a global contrast enhancement detection algorithm is proposed based on the shape an- alysis of the gray level histogram peak/gap bins. By means of the correspondence be tween the peak/gap bins position distribution and the involved pixel value mapping, a fast and efficient algorithm is proposed to estimate the gamma parameter blindly. As for the scenario that source regions suffer different contrast enhancement mappings, an effi-cient source-enhanced spliced image detection method is designed. Lastly, the security of existing contrast enhancement forensic algorithms is analyzed briefly. Test results demonstrate that our proposed contrast enhancement forensics methods could achieve good performance.
4) A semi non-intrusive forensic algorithm is proposed to identify the digital image resampling operator. As for the strictly monotone signal, its first derivative polarity regularity after suffering the traditional and geometric-dithering types of resampling is analyzed theoretically. Through designing suitable test pattern images, a unified resam-pling operator identification scheme is proposed. Test results show that the proposed method could identify the resampling operator embedded in softwares, as well as detect the anti-forensic type of resampling manipulation in some specific scenarios.
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