广西师范大学学报（自然科学版） ›› 2024, Vol. 42 ›› Issue (3): 1-16.doi: 10.16088/j.issn.1001-6600.2023050703

• 综述 •    下一篇

图像USM锐化取证与反取证技术综述

赵洁, 宋爽, 武斌*   

  1. 天津城建大学 计算机与信息工程学院, 天津 300384
  • 收稿日期:2023-05-07 修回日期:2023-10-17 发布日期:2024-05-31
  • 通讯作者: 武斌(1966—), 男, 河北张家口人, 天津城建大学教授。E-mail: wubin@tcu.edu.cn
  • 基金资助:
    天津市科技计划项目重大科技专项(14ZCZDGX00868); 天津市重点研发计划科技支撑重点项目(19YFZCGX00130); 天津市企业科技特派员项目(19JCTPJC47200)

Overview of Image USM Sharpening Forensics and Anti-forensics Techniques

ZHAO Jie, SONG Shuang, WU Bin*   

  1. School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China
  • Received:2023-05-07 Revised:2023-10-17 Published:2024-05-31

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摘要： 反锐化掩膜(unsharp masking, USM)锐化是图像编辑中改善图像视觉清晰度的一种常用操作。然而,若将这一操作应用到篡改司法证据或者公共事件图片等场景,则会造成严重危害。近年来,图像取证领域的研究者已经提出许多USM锐化取证与反取证的方法,本文对该领域文献进行系统性综述。首先阐述USM锐化操作的基本原理,然后从算法实现原理角度对现有的USM锐化取证与反取证方法进行分类总结,接着介绍相关文献常用的数据集,并对现有方法的性能进行对比评价,最后分析现有方法面临的挑战并对未来的研究方向进行展望,主要包括以下几个方面:使用新的数据集,深入取证与反取证博弈的研究,深入研究锐化反取证,注重取证方案的执行效率,提高反取证的可靠性。

关键词: 反锐化掩膜, USM锐化, 锐化取证, 锐化反取证, 数字图像取证

Abstract: Unsharp masking (USM) sharpening is a common operation to improve image visual clarity in image edition,However, it can cause serious harm if applied to scenarios involving the tampering of forensic evidence or public event photographs. In recent years, many methods for USM sharpening and anti-sharpening in digital image forensics have been proposed by researchers. So, a systematic review of the literature in this field is provided by this paper. The fundamental principles of the USM sharpening operation are first explained in this paper, followed by the categorization and summary of existing USM sharpening and anti-sharpening methods based on algorithm implementation principles. Additionally, commonly used datasets in related literature are introduced, and performance evaluations of existing methods are conducted for comparative purposes. Finally, the challenges faced by existing methods are analyzed and future research directions are discussed, mainly including the use of updated datasets, in-depth research on the game of forensics and anti-forensics, in-depth research on sharpening anti-forensics, the issue on the efficiency of forensics scheme execution, and the improvement of the reliability of anti-forensics.

Key words: unsharp masking, USM sharpening, sharpening forensics, sharpening anti-forensics, digital image forensics

中图分类号:  TP391.41

[1] 翟佩华. 数字图像主动取证技术研究[D]. 武汉: 湖北工业大学, 2017.
[2] 周琳娜. 数字图像盲取证技术研究[D]. 北京: 北京邮电大学, 2007.
[3] LIN X, LI J H, WANG S L, et al. Recent advances in passive digital image security forensics: a brief review[J]. Engineering, 2018, 4(1): 29-39. DOI: 10.1016/j.eng.2018.02.008.
[4] NIE L, LIN C Y, LIAO K, et al. Unsupervised deep image stitching: reconstructing stitched features to images[J]. IEEE Transactions on Image Processing, 2021, 30: 6184-6197. DOI: 10.1109/TIP.2021.3092828.
[5] LI J, LI X L, YANG B, et al. Segmentation-based image copy-move forgery detection scheme[J]. IEEE Transactions on Information Forensics and Security, 2015, 10(3): 507-518. DOI: 10.1109/TIFS.2014.2381872.
[6] 刘洋, 张玉金, 张涛, 等. 基于卷积神经网络的随机因子重采样图像检测[J]. 光电子激光, 2023, 34(3): 232-240. DOI: 10.16136/j.joel.2023.03.0263.
[7] 黄方军, 万晨. JPEG图像重压缩检测综述[J]. 信号处理, 2021, 37(12): 2251-2260. DOI: 10.16798/j.issn.1003-0530.2021.12.002.
[8] 毕秀丽, 邱雨檬, 肖斌, 等. 基于统计特征的图像直方图均衡化检测方法[J]. 计算机学报, 2021, 44(2): 292-303. DOI: 10.11897/SP.J.1016.2021.00292.
[9] 胡万, 张玉金, 张涛, 等. 基于多残差学习与注意力融合的中值滤波检测[J]. 光电子激光, 2023, 34(1): 81-89. DOI: 10.16136/j.joel.2023.01.0160.
[10] 顾雨舟. 基于稀疏编码的图像锐化操作的检测算法[D]. 上海: 上海交通大学, 2018. DOI: 10.27307/d.cnki.gsjtu.2018.003171.
[11] 王伟, 曾凤, 汤敏, 等. 数字图像反取证技术综述[J]. 中国图象图形学报, 2016, 21(12): 1563-1573. DOI: 10.11834/jig.20161201.
[12] 何沛松, 李伟创, 张婧媛, 等. 面向GAN生成图像的被动取证及反取证技术综述[J]. 中国图象图形学报, 2022, 27(1): 88-110. DOI: 10.11834/jig.210430.
[13] QURESHI M A, EL-ALFY E S M. Bibliography of digital image anti-forensics and anti-anti-forensics techniques[J]. IET Image Processing, 2019, 13(11): 1811-1823. DOI: 10.1049/iet-ipr.2018.6587.
[14] VANMALI A V, KATARIA T, KELKAR S G, et al. Analysis of ringing artifact in image fusion using directional wavelet transforms[J]. International Journal of Engineering Research and Technology, 2021, 9(3): 495-502. DOI: 10.17577/IJERTCONV9IS03102.
[15] CAO G, ZHAO Y, NI R R. Detection of image sharpening based on histogram aberration and ringing artifacts[C]// Proceedings of the 2009 IEEE International Conference on Multimedia and Expo. Los Alamitos, CA: IEEE Computer Society, 2009: 1026-1029. DOI: 10.1109/ICME.2009.5202672.
[16] 王哲. 基于多尺度特征的图像拼接检测及锐化取证[D]. 大连: 大连理工大学, 2011.
[17] CHANG C C, LIN C J. LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 27. DOI: 10.1145/1961189.1961199.
[18] CAO G, ZHAO Y, NI R R, et al. Unsharp masking sharpening detection via overshoot artifacts analysis[J]. IEEE Signal Processing Letters, 2011, 18(10): 603-606. DOI: 10.1109/LSP.2011.2164791.
[19] ZHU N, DENG C, GAO X B. Image sharpening detection based on multiresolution overshoot artifact analysis[J]. Multimedia Tools and Applications, 2017, 76(15): 16563-16580. DOI: 10.1007/s11042-016-3938-5.
[20] DING F, ZHU G P, SHI Y Q. A novel method for detecting image sharpening based on local binary pattern[C]// Digital-Forensics and Watermarking. Berlin: Springer, 2014: 180-191. DOI: 10.1007/978-3-662-43886-2_13.
[21] CHANG C C, HORNG J H, KAO W J. A secure extended LBP data hiding scheme based on octagon-shaped shell[J]. International Journal of Embedded Systems, 2021, 14(5): 497-508. DOI: 10.1504/IJES.2021.120277.
[22] WANG Y X, HE X K, JIANG Y D, et al.New image reconstruction algorithm for CCERT: LBP + Gaussian mixture model (GMM) clustering[J]. Measurement Science & Technology, 2021, 32(2): 024001. DOI: 10.1088/1361-6501/abbb66.
[23] DING F, ZHU G P, YANG J Q, et al. Edge perpendicular binary coding for USM sharpening detection[J]. IEEE Signal Processing Letters, 2015, 22(3): 327-331. DOI: 10.1109/LSP.2014.2359033.
[24] DING F, ZHU G P, DONG W Q, et al. An efficient weak sharpening detection method for image forensics[J]. Journal of Visual Communication and Image Representation, 2018, 50: 93-99. DOI: 10.1016/j.jvcir.2017.11.009.
[25] GU Y Z, WANG S L, LIN X, et al. USM sharpening detection based on sparse coding[C]// 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA). Los Alamitos, CA: IEEE Computer Society, 2016: 1-5. DOI: 10.1109/DICTA.2016.7797092.
[26] ANCUTI C O, ANCUTI C, DE VLEESCHOUWER C, et al. Color balance and fusion for underwater image enhancement[J]. IEEE Transactions on Image Processing, 2018, 27(1): 379-393. DOI: 10.1109/TIP.2017.2759252.
[27] YAN Y N, LI C L, PAN X X. An improved USM sharpening detection method for underwater images[C]// 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). Los Alamitos, CA: IEEE Computer Society, 2021: 1-5. DOI: 10.1109/CISP-BMEI53629.2021.9624237.
[28] LIN C L, SU C Y. Modified unsharp masking detection using Otsu thresholding and Gray code[C]// 2016 IEEE International Conference on Industrial Technology (ICIT). Los Alamitos, CA: IEEE Computer Society, 2016: 787-791. DOI: 10.1109/ICIT.2016.7474851.
[29] GAO H, HU M T, GAO T G, et al. An effective image detection algorithm for USM sharpening based on Pixel-Pair histogram[C]// Advances in Multimedia Information Processing-PCM 2018: LNCS Volume 11165. Cham: Springer, 2018: 396-407. DOI: 10.1007/978-3-030-00767-6_37.
[30] WANG D P, GAO T G. An efficient USM sharpening detection method for small-size JPEG image[J]. Journal of Information Security and Applications, 2020, 51: 102451. DOI: 10.1016/j.jisa.2020.102451.
[31] YE J Y, SHEN Z Y, BEHRANI P, et al. Detecting USM image sharpening by using CNN[J]. Signal Processing: Image Communication, 2018, 68: 258-264. DOI: 10.1016/j.image.2018.04.016.
[32] ZHAO J, SONG S, WU B. An efficient USM weak sharpening detection method for small size image forensics[J]. International Journal of Network Security, 2023, 25(1): 175-183. DOI: 10.6633/IJNS.202301_25(1).20.
[33] LU L J, YANG G B, XIA M. Anti-Forensics for unsharp masking sharpening in digital images[J]. International Journal of Digital Crime and Forensics, 2013, 5(3): 53-65. DOI: 10.4018/jdcf.2013070104.
[34] SHEN Z Y, DING F, SHI Y Q. Anti-forensics of image sharpening using generative adversarial network[C]// Digital Forensics and Watermarking: LNCS Volume 12022. Cham: Springer, 2020: 150-157. DOI: 10.1007/978-3-030-43575-2_12.
[35] WU J Y, WANG Z, ZENG H, et al. Multiple-operation image anti-forensics with WGAN-GP framework[C]// 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). Los Alamitos, CA: IEEE Computer Society, 2019: 1303-1307. DOI: 10.1109/APSIPAASC47483.2019.9023173.
[36] WU J Y, SUN W. Towards multi-operation image anti-forensics with generative adversarial networks[J]. Computers & Security, 2021, 100: 102083. DOI: 10.1016/j.cose.2020.102083.
[37] DONG J, WANG W, TAN T N. CASIA image tampering detection evaluation database[C]// 2013 IEEE China Summit and International Conference on Signal and Information Processing. Los Alamitos, CA: IEEE Computer Society, 2013: 422-426. DOI: 10.1109/ChinaSIP.2013.6625374.
[38] SCHAEFER G, STICH M. UCID: an uncompressed color image database[C]// Proceedings Volume 5307: Storage and Retrieval Methods and Applications for Multimedia 2004. Bellingham, WA: SPIE, 2003: 472-480. DOI: 10.1117/12.525375.
[39] US Natural Resources Conservation Service. Natural resource conservation service photo gallery[DB/OL]. 2013[2023-05-07]. https://photogallery.nrcs.usda.gov/res/sites/photogallery/.
[40] BAS P, FILLER T, PEVNY′ T. “Break our steganographic system”: the ins and outs of organizing BOSS[C]// Information Hiding. Berlin: Springer, 2011: 59-70. DOI: 10.1007/978-3-642-24178-9_5.
[41] LIU Y H, ZHAO Y, NI R R. Forensics of image blurring and sharpening history based on NSCT domain[C]// Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific. Los Alamitos, CA: IEEE Computer Society, 2014: 1-4. DOI: 10.1109/APSIPA.2014.7041728.
[42] DING F, DONG W Q, ZHU G P, et al. An advanced texture analysis method for image sharpening detection[C]// Digital-Forensics and Watermarking: LNCS Volume 9569. Cham: Springer, 2016: 72-82. DOI: 10.1007/978-3-319-31960-5_7.
[43] CHEN Y F, KANG X G, WANG Z J, et al. Densely connected convolutional neural network for multi-purpose image forensics under anti-forensic attacks[C]// Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security. New York, NY: Association for Computing Machinery, 2018: 91-96. DOI: 10.1145/3206004.3206013.
[44] WANG P, LIU F L, YANG C F. Thresholding binary coding for image forensics of weak sharpening[J]. Signal Processing: Image Communication, 2020, 88: 115956. DOI: 10.1016/j.image.2020.115956.
[45] BAYAR B, STAMM M C. Design principles of convolutional neural networks for multimedia forensics[J]. Electronic Imaging, 2020, 29(7): 77-86. DOI: 10.2352/ISSN.2470-1173.2017.7.MWSF-328.
[46] ZENG H, KANG X G, PENG A J. A multi-purpose countermeasure against image anti-forensics using autoregressive model[J]. Neurocomputing, 2016, 189: 117-122. DOI: 10.1016/j.neucom.2015.12.089.
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