谭舜泉 Dr. Shunquan Tan 

教授,中国共产党党员,研究生新锐导师,深圳市高层次专业人才,深圳市媒体信息内容安全重点实验室副主任IEEE高级会员,IEEE信息安全与取证技术委员会委员,中国图象图形学学会数字媒体取证与安全专委会委员,国际期刊EURASIP Journal on Information Security副主编。

联系方式(Contact):

Email: tansq@szu.edu.cn
Tel:0755-86716669

地址:深圳市南山区白石路深圳大学电子与信息工程学院N801,计算机与软件学院935


简介(Biography):

谭舜泉,男,工学博士,教授,中国共产党党员,研究生新锐导师,深圳市高层次专业人才,深圳市媒体信息内容安全重点实验室副主任IEEE高级会员,IEEE信息安全与取证技术委员会委员,中国图象图形学学会数字媒体取证与安全专委会委员。国际期刊EURASIP Journal on Information Security副主编,主持国家自然科学基金面上项目2项,作为重要成员参与国家自然科学基金联合基金重点项目2项。发表包括国际权威期刊IEEE TIFSIEEE TCSVTIEEE TDSCSCI/EI论文近70篇。自2018年来Google Scholar引用约2400次,h指数22。最高单篇论文引用近300次,引用200次以上6篇。所参与的项目“信息隐藏理论与方法”荣获2019年中国计算机协会科学技术奖自然科学一等奖,“图像隐写安全关键技术”荣获2022年广东省计算机学会科学技术奖自然科学奖二等奖,“自适应信息隐写安全理论与方法”荣获2022年深圳市科学技术奖自然科学奖二等奖

研究兴趣(Research Interests)

研究兴趣在以下领域:多媒体取证、多媒体安全、深度学习、机器学习、信息隐藏、图像/音频/视频信号处理、模式识别。近五年来一直从事深度学习框架(包括深度卷积神经网络、递归神经网络、强化学习模型)在多媒体取证、多媒体安全领域的应用,DeepFake等深度学习人脸篡改技术的检测及对抗。学界中首次提出基于深度学习的隐写分析模型(APSIPA 2014);首次提出将截断量化层与深度卷积神经网络结合起来的大尺度多媒体取证模型(TIFS 2018);首次提出WISERNet,一种结合逐通道卷积的深度学习真彩图像隐写分析及取证模型(TIFS 2019);首次把GAN(生成对抗网络)应用于多媒体安全,提出一种基于生成对抗网络的信息隐藏模型(SPL 2018);首次提出基于深度学习对抗样本的最小失真信息隐藏框架(TIFS 2019);首次提出应用于媒体信息安全的深度学习压缩框架(TIFS 2021);首次提出基于蒙特卡洛搜索树的非加性失真隐写强化学习框架;首次提出结合自对抗训练及篡改注意力机制的图像篡改定位模型(TIFS 2022);首次提出基于演员-评论家算法的非对称失真优化加性隐写框架(TIFS 2023)。并成功的把研究成果和产业化相结合。部分研究成果已经应用于和阿里巴巴的合作项目中。

学生获奖:


·         卓龙(本科生),谭舜泉,李斌,黄继武,荣获2023年度广东省计算机学会优秀论文奖一等奖65e914633f17d.png


·         陈涵,陈保营,胡彦杰,罗盛海,DeepFake Game Competition (DFGC)比赛和 IJCB 2021联合举办的检测赛道比赛中荣获第一名65e91469b38fc.png


·         陈保营,黄远坤,梅思玉,荣获2021年中国图像图形学学会CSIG图像图形技术挑战赛多媒体伪造取证大赛音频赛道冠军

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·         陈保营,庄培裕,黎思力,ECCV 2020 DeeperForensics Challenge 2020 荣获第一名

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教育背景及访学经历:

2005/10 - 2006/11,美国新泽西理工学院担任访问学者一年,合作导师:施云庆教授,IEEE Life Fellow.

2002/09 - 2007/07,中山大学,计算机科学系,博士,导师:黄继武教授

科研项目:

1.  国家自然科学基金面上项目,“面向真实环境的轻量化鲁棒图像篡改取证研究”,(62272314)2023.01-2026.12,(主持);

2.  国家自然科学基金联合基金项目重点支持项目,“社交网络虚假媒体内容检测关键技术的研究”,(U19B2022),254万,2020.01-2023.12,(排名第二)

3.  国家自然科学基金面上项目,“基于张量分解框架的深度学习信息隐藏对抗研究”,(61772349)2018.01-2021.12,(主持);

4.  国家自然科学基金——通用技术基础研究联合基金重点项目,“基于大数据的信息隐藏与对抗技术”,(U1636202)2017.1-2020.12,(排名第四);

5.  国家自然科学基金青年基金, “大尺度分布式深度学习框架在隐写分析上的应用”,(61402295) 2015.01-2017.12,(主持);

6.  广东省深圳市基础研究项目, “基于大尺度分布式深度学习框架的隐写分析研究”,(JCYJ20140418182819173) 2014.09-2016.09,(主持);

学术成果:

期刊论文:

·         胡林辉,陈保营,谭舜泉*,李斌,基于Convnext-Upernet的图像篡改检测定位模型,计算机学报,已录用,计算领域高质量科技期刊T1,CCF A类中文科技期刊

·         X. Mo, S. Tan*, W. Tang, B. Li and J. Huang, “ReLOAD: Using Reinforcement Learning to Optimize Asymmetric Distortion for Additive Steganography.” IEEE Transactions on Information Forensics and Security 18 (2023): 1524-1538(CCF A类期刊/中科院SCI一区/TOP期刊)

·         K. Wei, W. Luo, S. Tan, and J. Huang. “Universal Deep Network for Steganalysis of Color Image Based on Channel Representation.” IEEE Transactions on Information Forensics and Security 17 (2022): 3022-3036(CCF A类期刊/中科院SCI一区/TOP期刊)

·         G. Li, B. Li, S. Tan, and G. Qiu, “Learning deep co-occurrence features”, IEEE Transactions on Circuits and Systems for Video Technology, 2022,33(4):1610-1623 (中科院SCI一区/TOP期刊)

·         H. Chen, Y. Lin, B. Li, and S. Tan. “Learning Features of Intra-consistency and Inter-diversity: Keys towards Generalizable Deepfake Detection.” IEEE Transactions on Circuits and Systems for Video Technology, 2022, 33(3) :1468-1480(中科院SCI一区/TOP期刊)

·         X. Qin, B. Li, S. Tan, W. Tang, and J. Huang. Gradually Enhanced Adversarial Perturbations on Color Pixel Vectors for Image Steganography [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(8):5110-5123 (中科院SCI一区/TOP期刊)

·         L. Zhuo, S. Tan*, B. Li, J. Huang. Self-adversarial Training Incorporating Forgery Attention for Image Forgery Localization [J]. IEEE Transactions on Information Forensics and Security, 2022, 17: 819-834 (CCF A类期刊/中科院SCI一区/TOP期刊)

·         P. Zhuang, H. Li, S. Tan, B. Li, J. Huang. Image Tampering Localization Using a Dense Fully Convolutional Network [J]. IEEE Transactions on Information Forensics and Security, 2021, 16: 2986-2999 (CCF A类期刊/中科院SCI一区/TOP期刊)

·         X. Mo, S. Tan*, B. Li, J. Huang. MCTSteg: A Monte Carlo Tree Search-based Reinforcement Learning Framework for Universal Non-additive Steganography [J]. IEEE Transactions on Information Forensics and Security, 2021, 16: 4306-4320 (CCF A类期刊/中科院SCI一区/TOP期刊)

·         Q. Li, S. Chen, S. Tan*, B. Li, J. Huang. One-Class Double Compression Detection of Advanced Videos Based on Simple Gaussian Distribution Model [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 32(4): 2496-2500 (中科院SCI一区/TOP期刊)

·         S. Tan, W. Wu, Z. Shao, Q. Li, B. Li, J. Huang. CALPA-NET: Channel-pruning-assisted Deep Residual Network for Steganalysis of Digital Images  [J]. IEEE Transactions on Information Forensics and Security, 2021, 16: 131-146 (CCF A类期刊/中科院SCI一区/TOP期刊)

·         J. Zeng, S. Tan*, G. Liu, B. Li, J. Huang. WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images [J]. IEEE Transactions on Information Forensics and Security, 2019, 10(14): 2735-2748 (CCF A类期刊/中科院SCI一区/TOP期刊)

·         W. Tang, B. Li, S. Tan, M. Barni, J. Huang. CNN-based Adversarial Embedding for Image Steganography [J]. IEEE Transactions on Information Forensics and Security, 2019, 14(8): 2074-2087(CCF A类期刊/中科院SCI一区/TOP期刊)

·         J. Zeng, S. Tan*, B. Li, J. Huang. Large-scale JPEG Image Steganalysis using Hybrid Deep-learning Framework [J]. IEEE Transactions on Information Forensics and Security, 2018, 13(5): 1242-1257.(CCF A类期刊/中科院SCI一区/TOP期刊)

·         B. Li, Z. Li, S. Zhou, S. Tan, X. Zhang. New Steganalytic Features for Spatial Image Steganography based on Derivative Filters and Threshold LBP Operator [J]. IEEE Transactions on Information Forensics and Security, 2018, 13(5): 1242-1257.(CCF A类期刊/中科院SCI一区/TOP期刊)

·         B. Li, W. Wei, A. Ferreira, S. Tan. ReST-Net: Diverse Activation Modules and Parallel Subnets-Based CNN for Spatial Image Steganalysis [J]. IEEE Signal Processing Letters, 2018, 25(5): 650-654.(CCF C类期刊/中科院SCI三区)

·         史晓裕, 李斌, 谭舜泉. 深度学习空域隐写分析的预处理层 [J]. 应用科学学报, 2018, 36(2):309-320.

·         S. Tan, H. Zhang, B. Li, J. Huang. Pixel-Decimation-Assisted Steganalysis of Synchronize-Embedding-Changes Steganography[J]. IEEE Transactions on Information Forensics and Security, 2017, 12(7): 1658-1670.(CCF A类期刊/中科院SCI一区/TOP期刊)

·         W. Tang, S. Tan*, B. Li, J. Huang. Automatic Steganographic Distortion Learning Using a Generative Adversarial Network [J]. IEEE Signal Processing Letters, 2017, 24(10): 1547–1551.(CCF C类期刊/中科院SCI三区)

·         S. Chen, S. Tan*, B. Li, J. Huang. Automatic Detection of Object-based Forgery in Advanced Video [J]. IEEE Transactions on Circuits and Systems for Video Technology. 2016, 26(11): 2138-2151.(CCF B类期刊/中科院SCI二区)

·         B. Li, T.-T. Ng, X. Li, S. Tan, J. Huang. Statistical Model of JPEG Noises and Its Application in Quantization Step Estimation [J]. IEEE Transactions on Image Processing. 2015, 24(5): 1471–1484.(CCF A类期刊/中科院SCI二区)

·         B. Li, M. Wang, X. Li, S. Tan, J. Huang, A Strategy of Clustering Modification Directions in Spatial Image Steganography [J]. IEEE Transactions on Information Forensics and Security. 2015, 10(9): 1905–1917.(CCF A类期刊/中科院SCI一区)

·         B. Li, T.-T. Ng, X. Li, S. Tan, J. Huang. Revealing the Trace of High-quality JPEG Compression through Quantization Noise Analysis [J]. IEEE Transactions on Information Forensics and Security. 2015 10(3): 558-573.(CCF A类期刊/中科院SCI一区)

·         B. Li, S. Tan, M. Wang, J. Huang. Investigation on Cost Assignment in Spatial Image Steganography [J]. IEEE Transactions on Information Forensics and Security. 2014 9(8): 1264–1277.(CCF A类期刊/中科院SCI一区)

·         S. Tan, B. Li. Targeted Steganalysis of Edge Adaptive Image Steganography Based on LSB Matching Revisited Using B-spline Fitting [J]. IEEE Signal Processing Letters. 2012, 19(6): 336-339.(CCF C类期刊/中科院SCI三区)

会议论文:

·         X. Mo, S. Tan*, B. Li, and J. Huang, “Poster: Query-efficient black-box attack for image forgery localization via reinforcement learning”, ACM CCS 2023 (poster), accepted. CCF A

·         G. Li, B. Li, C. Chen, S. Tan, and G. Qiu. “Learning General Gaussian Mixture Model with Integral Cosine Similarity.” In Proc. the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22), Vienna, Austria, July, 23-29, 2022. pp. 3201-3207.

·         X. Qin, S. Tan, W. Tang, B. Li, and J. Huang, “Image Steganography Based on Iterative Adversarial Perturbations onto a Synchronized Directions Sub-Image,” in Proc. ICASSP, Toronto, ON, Canada, Jun. 2021, pp. 2705–2709.

·         Q. Li, Z. Shao, S. Tan*, J. Zeng, and B. Li, “Non-structured Pruning for Deep-learning based Steganalytic Frameworks”, Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2019, Lanzhou, China, November, 18-21, 2019.

·         L. Zhuo, S. Tan*, J. Zeng, B. Li, “Fake Colorized Image Detection with Channel-wise Convolution based Deep-learning Framework”, Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2018, Honolulu, Hawaii, USA, November, 12-15, 2018.

·         H. Zhang, B. Li, S. Tan. “A New Steganographic Distortion Function with Explicit Considerations of Modification Interactions”, International Conference  on Cloud Computing and Security, Haikou, China, June 8-10, 2018.

·         S. Zhou, W. Tang, S. Tan, B. Li. “Content-adaptive Steganalysis via Augmented Utilization of Selection-channel Information”, 17th International Workshop on Digital Forensics and Watermarking, Jeju, Korea, October 22-24, 2018. 

·         H. Li, H. Chen, B. Li, S. Tan. “Can Forensic Detectors Identify GAN Generated Images?”, Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2018, Honolulu, Hawaii, USA, November, 12-15, 2018.

·         Y. Huang, S. Tan, B. Li, J. Huang. “VPCID-A VoIP Phone Call Identification Database”, 17th International Workshop on Digital Forensics and Watermarking, Jeju, Korea, October 22-24, 2018.

·         J. Zeng, S. Tan*, B. Li, “Pre-training via Fitting Deep Neural Network to Rich-model Features Extraction Procedure and Its Effect on Deep Learning for Steganalysis”, in Proc. Media Watermarking, Security, and Forensics, Part of IS&T International Symposium on Electronic Imaging (EI'2017), Burlingame, CA, Juanuary, 29-February, 2, 2017.

·         S. Tan, S. Chen, B. Li, “GOP Based Automatic Detection of Object-based Forgery in Advanced Video”, in Proc. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2015, HongKong, China, December 16-19, 2015.

·         S. Tan, B. Li, “Stacked Convolutional Auto-Encoders for Steganalysis of Digital Images”, in Proc. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2014, Siem Reap, Cambodia, December 9-12, 2014.

·          S. Tan, B. Li, “Targeted Steganalysis of Adaptive Pixel-value Differencing Steganography”, in Proc. 2012 IEEE International Conference on Image Processing. Orlando, Florida, USA, Oct. 2012.

·         S. Tan, “Steganalysis of LSB Matching Revisited for Consecutive Pixels using B-spline Functions”, Lecture Notes in Computer Science. v7128 LNCS, Digital Watermarking - 10th International Workshop, IWDW 2011, 2012, pp 112-126.

专利:

1.  谭舜泉,关雨呈,李斌,黄继武,基于对抗网络的二值图像隐写方法(ZL 202011553884.5),2023.11,中国

2.     谭舜泉,陈奕邻,李斌,黄继武,一种图像安全取证模型生成方法、取证方法及电子设备(ZL202110043628.X),2023.08,中国

3.     曾吉申,谭舜泉,莫显博,李斌,黄继武,一种基于深度学习的视频取证方法(ZL201910082603.3),2023.07,中国

4.     李振军,陆芸婷,王昌伟,谭舜泉,基于区块链的消息加密传输方法、装置、设备及介质(ZL202210315005.8),2023.05,中国

5.     谭舜泉,吴威龙,邵子龙,李斌,黄继武,一种图像隐写分析方法、智能终端及存储介质(ZL201911387659.6),2023.05,中国

6.     发明专利, 谭舜泉,卓龙,李斌,黄继武, 基于图像生成网络模型的图像处理方法、系统及存储介质(ZL201911400233.X),2023.05,中国

7.     发明专利, 彭荣煊,莫显博,谭舜泉,李斌,黄继武,一种秘密图像无密钥提取方法及相关设备(ZL202210934604.8),2022.11,中国

8.     发明专利, 谭舜泉,李秋实,陈盛达,李斌,黄继武,一种视频重压缩检测方法、终端设备及存储介质(ZL202011619108.0),2022.07,中国

9.     发明专利, 谭舜泉,李振军,莫显博,欧培,隐藏模型训练及使用方法、装置和计算机可读存储介质(ZL201810555366.3),2022.05,中国

10.  发明专利, 谭舜泉,卓龙,李斌,黄继武,一种图象处理模型生成方法、智能终端及存储介质(ZL201911424964.8),2022.05

11.  发明专利, 谭舜泉,陈奕邻,李秋实,李斌,黄继武,一种基于Tucker分解的图像隐写分析方法及终端(ZL202110337203X),2021.07,中国

12.  发明专利, 曾吉申,谭舜泉,李斌,黄继武,一种基于深度学习模型的隐写图像检测方法及系统(ZL201610923908.9),2020.01,中国

13.  发明专利, 张浩杰,谭舜泉,李斌,黄继武,一种基于频域分析的图像隐写方法及系统(ZL201610653147X),2019.06,中国

14.  发明专利, 陈盛达,黄继武,谭舜泉,一种H264视频内容篡改检测方法(ZL201410395140.3),2018.04,中国

软件著作权:

·         谭舜泉,李来源,深圳大学,车牌场景取证优化软件V1.0,2024SR0226973,原始取得,全部权利,2023-11-01

·         谭舜泉,胡彦杰, 深圳大学,语义合理的大规模篡改图像数据集自动生成软件V1.0, 2022SR0180893, 原始取得, 全部权利, 2021-12-18


联系方式

  • 电子邮件:liuwanqi@szu.edu.cn
  • 地址:深圳市南山区南海大道3688号深圳大学电子与信息工程学院N801
  • 邮编:518060
  • 电话:0755-86716669
  • E-mail: liuwanqi@szu.edu.cn
  • Address: Room N801, College of Electronics and Information Engineering, Shenzhen University
  • Postcode: 518060
  • Tel: 0755-86716669