Journal of Guangxi Normal University(Natural Science Edition) ›› 2024, Vol. 42 ›› Issue (3): 1-16.doi: 10.16088/j.issn.1001-6600.2023050703
ZHAO Jie, SONG Shuang, WU Bin*
[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. |
[1] | XIAO Yuting, LÜ Xiaoqi, GU Yu, LIU Chuanqiang. Classification of Diabetic Retinopathy Based on Split Residual Network [J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(1): 91-101. |
[2] | ZENG Liang, HU Qian, YANG Tengfei, TAN Weiwei. Substation Personnel Safety Operation Detection Based on L-ConvNeXt Network [J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(1): 102-110. |
[3] | XI Lingfei, Yilihamu Yaermaimaiti, LIU Yajie. Surface Defect Detection Method for Aluminum Profile Based on Improved YOLOv5 [J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(1): 111-119. |
[4] | HUANG Yeqi, WANG Mingwei, YAN Rui, LEI Tao. Surface Quality Detection of Diamond Wire Based on Improved YOLOv5 [J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(4): 123-134. |
[5] | LIANG Zhenfeng, XIA Haiying. A Fast Stitching Algorithm for UAV Aerial Images [J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(3): 41-52. |
[6] | HAN Xinyue, DENG Changzheng, FU Tian, XIA Pengyu, LIU Xuan. Transient Electromagnetic Defect Identification of Grounding Grid Based on MWOA-Elman Neural Network [J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(3): 53-66. |
[7] | QIAN Youwei, HE Fuyun, WEI Yan, FENG Huiling, HU Cong. Neuron Image Segmentation Based on Dual Coding Path Fusion and Bidirectional ConvLSTM [J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(3): 67-79. |
[8] | LI Yang, GOU Gang. Lightweight Garbage Detection Method Based on Improved YOLOX [J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(3): 80-90. |
[9] | WANG Luna, DU Hongbo, ZHU Lijun. Stacked Capsule Autoencoders Optimization Algorithm Based on Manifold Regularization [J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(2): 76-85. |
[10] | WEI Mingjun, ZHOU Taiyu, JI Zhanlin, ZHANG Xinnan. Detection Method of Mask Wearing in Public Places Based on YOLOv3 [J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(1): 76-86. |
[11] | NIU Xuede, GAO Bingpeng, REN Rongrong, XU Mingming. Crop Pestsand Diseases Identification and Android Application Based on Lightweight CNN [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(6): 59-68. |
[12] | YU Mengzhu, TANG Zhenjun. Survey of Video Hash Research Based on Hand-craft Features [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(5): 72-89. |
[13] | LIANG Qihua, HU Xiantao, ZHONG Bineng, YU Feng, LI Xianxian. Research Progress of Target Tracking Algorithm Based on Siamese Network [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(5): 90-103. |
[14] | LI Zhixin, SU Qiang. Knowledge-aided Image Captioning [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(5): 418-432. |
[15] | WAN Liming, ZHANG Xiaoqian, LIU Zhigui, SONG Lin, ZHOU Ying, LI Li. CT Image Segmentation of UNet Pulmonary Nodules Based on Efficient Channel Attention [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 66-75. |
|