基于感知哈希的图像认证算法研究
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摘要
数字图像处理技术的进步为图像信息的安全性提出了新的挑战,这使得图像认证成为一个亟待解决的问题。图像认证技术,作为一种有效的图像信息安全技术,为图像了提供一种保护手段。但是传统的图像认证和加密手段很难满足其应用的要求,图像认证本质上关注的应该是图像所传达的感知信息而非图像的表现形式。因此基于感知哈希的图像认证技术,是目前解决图像安全问题的一种有效途径。
基于感知哈希的图像认证技术,扩展了传统图像认证的认证模式,多样化并细化了图像认证的认证粒度。但作为一门新兴的技术,感知哈希技术虽然受到越来越多的关注,在理论和实际应用中仍然存在着诸多的问题。目前感知哈希的研究缺乏对感知特性的充分考虑,且在追求鲁棒的同时忽略了对安全性的认识,使得很多感知哈希算法无法满足图像认证的基本要求。
针对上述问题,本文首先系统的综述了感知哈希理论以及研究现状,提出了应用于图像认证的感知哈希算法所应具备的必要条件,探讨了基于感知哈希的图像认证算法的目标与具体流程,进而提出了两种不同方式和粒度的基于感知哈希的图像认证及篡改检测算法。论文的主要研究工作和创新点如下:
提出了一种基于contourlet变换的图像感知哈希认证算法。在该算法中,先后提出了旋转快速校正机制和半扩散的概念,并首次提出将contourlet变换引入以提取图像感知特征。旋转快速校正机制用简单的方法实现旋转图像的校正工作;半扩散将完全扩散与不扩散折中形成适合于图像认证的感知哈希算法应用的安全解决方案。
提出了一种基于结构略图的图像感知哈希认证算法。把基于contourlet特征的结构略图作为感知信息提取,利用结构略图对内容变化的察觉能力,构造了一个可以抵抗恶意篡改的鲁棒图像认证方案,并利用感知信息对图像内容的携带能力,实现了基于内容的图像篡改定位与恢复。
研究了图像感知哈希认证算法的安全性,提出了对安全性的理解。并结合各种对安全性的分析方法,分别从理论上和抗攻击实验上对现有图像感知哈希认证算法和本文提出的算法进行了比较和分析。
The development of digital image processing techniques has brought many newchallenges to image security. As such, image information protection has become one ofthe most important research problems. There exist many image authentication techniquesthat could solve the problem effectively. Traditional image authentication and encryptiontechniques are usually based on the bit stream of an image file. However, their identifi-cation degrades seriously in the process of transmission and storage. Therefore, imageauthentication techniques based on perceptual hashing are proposed.
The perceptual hashing models expand the traditional models, diversify and refinethe granularity of the image authentication. Although the perceptual models outperformthe traditional bit stream models, they still have two main limitations. First, there areinadequate perceptual features to support the models; second, the security is not wellconsidered along with the high robustness.
To overcome the above limitations, we firstly made a detailed survey on existing per-ceptual hashing algorithms, based on which, we analyzed the requirements of algorithmdesign. Then we proposed a novel framework of image authentication and designed twonew perceptual hashing algorithms with regarding to different patterns and granularities.Compared with previous work, we have made the following three contributions:
First, we proposed an algorithm based on contourlet transform. In this algorithm,we defined two new concepts”fast rotation registration scheme”and”semi-diffusion”.The fast rotation registration scheme turns the rotated image into the original style whilesemi-diffusion combines the diffusion and non-diffusion to improve the security of imageauthentication. To the best of our knowledge, we are the first to use contourlet transfor-mation for perceptual feature extraction.
Second, we proposed an algorithm based on structure skeleton. We took the struc-ture skeleton of contourlet features as the perceptual information. The structure skeletoncould identify the content changes of the images, which makes our image authenticationscheme resistant to tampering. The tamper localization is achieved by processing theinformation of the perceptual features.
Finally, we investigated the existing security standards of image authentication based on perceptual hashing, based on which we defined our security standard. Thenwe analyzed our algorithms against existing work according to the security standard.
基于感知哈希的图像认证技术,扩展了传统图像认证的认证模式,多样化并细化了图像认证的认证粒度。但作为一门新兴的技术,感知哈希技术虽然受到越来越多的关注,在理论和实际应用中仍然存在着诸多的问题。目前感知哈希的研究缺乏对感知特性的充分考虑,且在追求鲁棒的同时忽略了对安全性的认识,使得很多感知哈希算法无法满足图像认证的基本要求。
针对上述问题,本文首先系统的综述了感知哈希理论以及研究现状,提出了应用于图像认证的感知哈希算法所应具备的必要条件,探讨了基于感知哈希的图像认证算法的目标与具体流程,进而提出了两种不同方式和粒度的基于感知哈希的图像认证及篡改检测算法。论文的主要研究工作和创新点如下:
提出了一种基于contourlet变换的图像感知哈希认证算法。在该算法中,先后提出了旋转快速校正机制和半扩散的概念,并首次提出将contourlet变换引入以提取图像感知特征。旋转快速校正机制用简单的方法实现旋转图像的校正工作;半扩散将完全扩散与不扩散折中形成适合于图像认证的感知哈希算法应用的安全解决方案。
提出了一种基于结构略图的图像感知哈希认证算法。把基于contourlet特征的结构略图作为感知信息提取,利用结构略图对内容变化的察觉能力,构造了一个可以抵抗恶意篡改的鲁棒图像认证方案,并利用感知信息对图像内容的携带能力,实现了基于内容的图像篡改定位与恢复。
研究了图像感知哈希认证算法的安全性,提出了对安全性的理解。并结合各种对安全性的分析方法,分别从理论上和抗攻击实验上对现有图像感知哈希认证算法和本文提出的算法进行了比较和分析。
The development of digital image processing techniques has brought many newchallenges to image security. As such, image information protection has become one ofthe most important research problems. There exist many image authentication techniquesthat could solve the problem effectively. Traditional image authentication and encryptiontechniques are usually based on the bit stream of an image file. However, their identifi-cation degrades seriously in the process of transmission and storage. Therefore, imageauthentication techniques based on perceptual hashing are proposed.
The perceptual hashing models expand the traditional models, diversify and refinethe granularity of the image authentication. Although the perceptual models outperformthe traditional bit stream models, they still have two main limitations. First, there areinadequate perceptual features to support the models; second, the security is not wellconsidered along with the high robustness.
To overcome the above limitations, we firstly made a detailed survey on existing per-ceptual hashing algorithms, based on which, we analyzed the requirements of algorithmdesign. Then we proposed a novel framework of image authentication and designed twonew perceptual hashing algorithms with regarding to different patterns and granularities.Compared with previous work, we have made the following three contributions:
First, we proposed an algorithm based on contourlet transform. In this algorithm,we defined two new concepts”fast rotation registration scheme”and”semi-diffusion”.The fast rotation registration scheme turns the rotated image into the original style whilesemi-diffusion combines the diffusion and non-diffusion to improve the security of imageauthentication. To the best of our knowledge, we are the first to use contourlet transfor-mation for perceptual feature extraction.
Second, we proposed an algorithm based on structure skeleton. We took the struc-ture skeleton of contourlet features as the perceptual information. The structure skeletoncould identify the content changes of the images, which makes our image authenticationscheme resistant to tampering. The tamper localization is achieved by processing theinformation of the perceptual features.
Finally, we investigated the existing security standards of image authentication based on perceptual hashing, based on which we defined our security standard. Thenwe analyzed our algorithms against existing work according to the security standard.
引文
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2牛夏牧,焦玉华.感知哈希综述[J].电子学报, 2008, 36(7):1405–1411.
3 J. Dittmann, A. Steinmetz, R. Steinmetz. Content-based Digital Signature FormotionPictures Authentication and Content-fragile Watermarking[J]. 1999 IEEE Interna-tional Conference on Multimedia Computing and Systems, 1999, 2(2):209–213.
4吴海金.数字图像认证算法的安全性分析.数字图像认证算法的安全性分析[D]清华大学,2004:2–3.
5 H. Kobayashi, H. Kiya. Robust Image Authentication Using Hash Function[C]. Pro-ceedings of IEEE Region 10 Conference. 2004:A:435–438.
6 R. C.-W. Phan. Tampering with a Watermarking-based Image AuthenticationScheme[J]. Patter Recognition, 2008, 41:3493–3496.
7 B. Schneier.应用密码学-协议算法与c源程序[M] .北京:机械工业出版社,2003.
8 G. Friedman. The Trustworthy Digital Camera: Restoring Credibility to the Photo-graphic Image[J]. IEEE Transactions on Consumer Electronics, 1993, 39(4):905–910.
9 S. Bhattacha, M. Kutter. Compression Tolerant Image Authentication[J]. Proceed-ings of ICIP, Chicago, 1998, 1:435–439.
10 C. De Roover, C. De Vleeschouwer, F. Lefebvre, et al. Robust Image Hashing Basedon Radial Variance of Pixels[C]. IEEE International Conference on Image Process-ing. 2005, 3:III–77–80.
11 RIAA/IFPI. Request for Information on Audio Fingerprinting Technologies[J]. http://www.riaa.com, 2001.
12 P. Cano, E. Batlle, E. Gomez, et al. Audio Fingerprinting: Concepts and Applica-tions[C]. Proceedings of 1st International Conference on Fuzzy Systems and Knowl-edge Discovery. 2002.
13 J. S. Seo, J. Haitsma, T. Kalker, et al. A Robust Image Fingerprinting SystemUsing the Radon Transform[J]. Signal Processing: Image Communication, 2004,19(4):325–339.
14 F. Mapelli, R. Pezzano, R. Lancini. Robust Audio Fingerprinting for Song Identifi-cation[C]. 2004.
15 M. Mohri, P. Moreno, E. Weinstein. Robust Music Identification, Detection, andAnalysis[J]. 2007.
16 J. Haitsma, T. Kalker. A Highly Robust Audio Fingerprinting System[C]. 3rd Inter-national Conference on Music Information Retrieval, ISMIR. Paris, France, 2002.
17 P. Cano, E. Batle, T. Kalker. A Review of Algorithms for Audio Fingerprinting[C].IEEE Workshop on Multimedia Signal Processing. 2002:169–173.
18 Unicity Distance of Robust Image Hashing[J]. IEEE Transactions on InformationForensics and Security, 2007, 2(3):462–467.
19 H. Ozer, B. Sankur, N. Memon. Robust Audio Hashing for Audio Identification[C].European Signal Processing Conference (EUSIPCO). 2004.
20 F. Ahmed, M. Y. Siyal. A Secure and Robust Dct-based Hashing Scheme for ImageAuthentication[C]. Communication systems, 2006. ICCS 2006. 10th IEEE Singa-pore International Conference on. 2006:1–6.
21 L. Ghouti, A. Bouridane. A Robust Perceptual Audio Hashing Using Balanced Mul-tiwavelets[C]. Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Pro-ceedings. 2006 IEEE International Conference on. 2006, 5:V–V.
22 S.-H. Yang, C.-F. Chen. Robust Image Hashing Based on Spiht[C]. InformationTechnology: Research and Education, 2005. ITRE 2005. 3rd International Confer-ence on. 2005:110–114.
23 V. Monga, M. Mihcak. Robust Image Hashing via Non-negative Matrix Factoriza-tions[C]. Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceed-ings. 2006 IEEE International Conference on. 2006, 2:II–II.
24 J. Fridrich, M. Goljan. Robust Hash Functions for Digital Watermarking[C]. In-formation Technology: Coding and Computing, 2000. Proceedings. InternationalConference on. 2000:178–183.
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