Review of JPEG recompression forensics for color images

doi:10.11772/j.issn.1001-9081.2024111614

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (11): 3609-3620.DOI: 10.11772/j.issn.1001-9081.2024111614

• Cyber security • Previous Articles

Review of JPEG recompression forensics for color images

Hao WANG¹, Jinwei WANG¹^,², Xin CHENG³(), Jiawei ZHANG⁴, Hao WU⁵, Xiangyang LUO⁵, Bin MA⁶

^1.School of Automation，Nanjing University of Science and Technology，Nanjing Jiangsu 210094，China
^2.College of Cryptology and Cyber Science，Nankai University，Tianjin 300350，China
^3.School of Computer Science，Cyber Science and Engineering，Nanjing University of Information Science and Technology，Nanjing Jiangsu 210044，China
^4.School of Cyber Security and Information Law，Chongqing University of Posts and Telecommunications，Chongqing 400065，China
^5.State Key Laboratory of Mathematical Engineering and Advanced Computing （Information Engineering University），Zhengzhou Henan 450001，China
^6.School of Cyber Security，Qilu University of Technology，Jinan Shandong 250353，China

Received:2024-11-13 Revised:2024-12-06 Accepted:2024-12-20 Online:2025-01-03 Published:2025-11-10
Contact: Xin CHENG
About author:WANG Hao， born in 1995， Ph. D.， lecturer. His research interests include information security， JPEG compression forensics.
WANG Jinwei， born in 1978， Ph. D.， professor. His research interests include artificial intelligence security， multimedia forensics.
ZHANG Jiawei， born in 1996， Ph. D.， lecturer. His research interests include adversarial sample forensics， JPEG compression forensics.
WU Hao， born in1999， Ph. D. candidate. His research interests include adversarial sample forensics， JPEG compression forensics.
LUO Xiangyang， born in 1978， Ph. D.， professor. His research interests include image steganography， steganalysis.
MA Bin， born in 1973， Ph. D.， professor. His research interests include reversible information hiding， multimedia forensics， steganography and steganalysis.
Supported by:
National Natural Science Foundation of China(62472229);Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX24_1513);Open Foundation of the State Key Laboratory of Tibetan Intelligent Information Processing and Application（Key Laboratory of Tibetan Information Processing, Ministry of Education）(2024-Z-003)

彩色图像JPEG重压缩取证综述

王昊¹, 王金伟¹^,², 程鑫³(), 张家伟⁴, 吴昊⁵, 罗向阳⁵, 马宾⁶

^1.南京理工大学自动化学院，南京 210094
^2.南开大学网络空间安全学院，天津 300350
^3.南京信息工程大学计算机学院、网络空间安全学院，南京 210044
^4.重庆邮电大学网络空间安全与信息法学院，重庆 400065
^5.数学工程与先进计算国家重点实验室（信息工程大学），郑州 450001
^6.齐鲁工业大学网络空间安全学院，济南 250353

通讯作者: 程鑫
作者简介:王昊（1995—），男，江苏连云港人，讲师，博士，主要研究方向：信息安全、JPEG压缩取证
王金伟（1978—），男，山东烟台人，教授，博士，主要研究方向：人工智能安全、多媒体取证
张家伟（1996—），男，江苏南京人，讲师，博士，主要研究方向：对抗样本取证、JPEG压缩取证
吴昊（1999—），男，江苏南京人，博士研究生，主要研究方向：对抗样本取证、JPEG压缩取证
罗向阳（1978—），男，湖北荆门人，教授，博士，主要研究方向：图像隐写、隐写分析
马宾（1973—），男，山东济南人，教授，博士，主要研究方向：可逆信息隐藏、多媒体取证、隐写与隐写分析。
基金资助:
国家自然科学基金资助项目(62472229);江苏省研究生研究与实际创新项目(KYCX24_1513);省部共建藏语智能信息处理及应用国家重点实验室（藏文信息处理教育部重点实验室）开放课题(2024-Z-003)

Abstract

Abstract:

The JPEG （Joint Photographic Experts Group） compression is one of the most widely used image compression standards and is involved in various forensic scenarios and security models such as image operation chain forensics， image source forensics， steganography， steganalysis， and JPEG anti-forensics. Researchers have conducted extensive studies on JPEG recompression forensics based on the characteristics of JPEG images， discovering that it not only provides prior knowledge for image forensics but also can be directly applied in forensic scenarios. Therefore， JPEG recompression forensics for color images was reviewed. Firstly， the research background of recompression forensics was introduced， and recompression forensics were classified into three types： non-aligned， aligned asynchronous， and aligned synchronous. Secondly， the basic knowledge required for recompression forensics was detailed， including the JPEG compression process， convergence error， error images， and algorithm evaluation metrics. Furthermore， existing methods for each type of problem were thoroughly reviewed and systematized. Additionally， since fields such as image steganography and adversarial examples involved robustness studies related to JPEG recompression， the applications of JPEG recompression features in these domains were highlighted， and common algorithms were compared to summarize their advantages and disadvantages. Finally， open challenges and future research directions in JPEG recompression forensics were discussed.

Key words: digital forensics, JPEG recompression forensics, JPEG compression process, error image, convergence error

摘要：

JPEG（Joint Photographic Experts Group）压缩是当前应用最为广泛的图像压缩标准之一，它涉及诸如图像操作链取证、图像源取证、隐写与隐写分析和JPEG反取证等多种取证场景和安全模型。研究者针对JPEG图像特性开展了诸多有关JPEG重压缩取证的研究，发现JPEG重压缩取证不仅为图像取证提供先验知识，也可以直接应用到取证场景中。因此，对彩色图像JPEG重压缩取证进行综述。首先，介绍了重压缩取证的研究背景，并将重压缩取证分成了非对齐、对齐异步和对齐同步3种重压缩问题；其次，详细介绍了JPEG压缩的流程、收敛误差、误差图像、算法评价指标等用于重压缩取证的基础知识；再次，对每类问题的现有方法进行了详细的介绍和梳理。此外，由于图像隐写、对抗样本等都涉及关于JPEG重压缩的鲁棒性研究，因此，列举了JPEG重压缩特征特性在上述领域中的应用，并选取了常见的算法进行对比，总结归纳它们的优缺点。最后，展望了JPEG重压缩取证中有待进一步解决的问题和发展趋势。

关键词: 数字取证, JPEG重压缩取证, JPEG压缩流程, 误差图像, 收敛误差

CLC Number:

TP391

Hao WANG, Jinwei WANG, Xin CHENG, Jiawei ZHANG, Hao WU, Xiangyang LUO, Bin MA. Review of JPEG recompression forensics for color images[J]. Journal of Computer Applications, 2025, 45(11): 3609-3620.

王昊, 王金伟, 程鑫, 张家伟, 吴昊, 罗向阳, 马宾. 彩色图像JPEG重压缩取证综述[J]. 《计算机应用》唯一官方网站, 2025, 45(11): 3609-3620.

Figures/Tables 10

Fig. 1 Research areas of JPEG compression forensics

Fig. 2 Process of JPEG compression and decompression

Fig. 3 Diagram of non-aligned JPEG recompression grid

Fig. 4 Acquisition process of En

Fig. 5 Convergence trend of Jn

Fig. 6 Variation of EBn under continuous compression at the same quality factor

Fig. 7 Convergence trend of En

Tab. 1 Performance comparison on UCID and NRCS datasets

$Q F 1$	算法	UCID数据集上的准确率/%					NRCS数据集上的准确率/%
$Q F 1$	算法	$Q F 2 = 50$	$Q F 2 = 60$	$Q F 2 = 70$	$Q F 2 = 80$	$Q F 2 = 90$	$Q F 2 = 50$	$Q F 2 = 60$	$Q F 2 = 70$	$Q F 2 = 80$	$Q F 2 = 90$
50	文献［43］算法	84.51	99.96	100.00	100.00	100.00	85.14	99.91	99.99	99.98	99.99
	文献［40］算法	87.17	89.01	96.86	99.54	99.70	86.94	87.79	96.58	99.69	99.81
	文献［1］算法	81.97	80.23	85.31	90.95	89.70	81.60	81.10	85.61	93.13	85.77
	文献［26］算法	51.61	100.00	99.95	99.99	100.00	50.82	99.99	99.99	100.00	100.00
	文献［14］算法	52.86	99.33	98.96	99.71	100.00	50.76	98.99	99.11	99.37	100.00
	文献［32］算法	50.33	92.57	94.95	98.33	99.84	51.45	96.79	95.16	97.67	99.26
60	文献［43］算法	99.77	84.42	100.00	99.99	99.99	99.59	84.7	99.99	99.98	99.98
	文献［40］算法	82.67	86.59	88.82	99.56	99.65	81.62	87.06	87.73	99.79	99.92
	文献［1］算法	68.31	82.18	81.50	81.40	86.07	62.10	81.40	82.07	89.72	82.28
	文献［26］算法	99.99	51.61	100.00	99.97	100.00	99.99	50.87	100.00	99.99	100.00
	文献［14］算法	98.53	50.06	98.71	99.97	99.99	98.48	50.79	97.63	99.48	100.00
	文献［32］算法	96.02	52.01	94.36	96.46	99.57	93.33	52.80	95.93	98.84	99.26
70	文献［43］算法	99.92	99.79	86.40	99.99	100.00	99.83	99.90	85.23	100.00	100.00
	文献［40］算法	84.25	82.73	88.37	99.31	98.92	85.17	82.87	89.28	99.46	99.13
	文献［1］算法	61.23	64.29	82.34	94.46	73.21	61.62	61.33	83.40	94.10	70.98
	文献［26］算法	99.99	99.99	51.22	99.99	100.00	100.00	100.00	50.89	99.97	100.00
	文献［14］算法	98.91	99.21	53.44	99.89	100.00	98.79	99.41	50.74	99.12	100.00
	文献［32］算法	95.12	90.83	51.04	98.9	99.91	90.88	89.53	50.26	97.25	99.54
80	文献［43］算法	99.57	99.91	99.99	98.43	99.99	99.60	99.93	99.97	98.56	99.99
	文献［40］算法	75.42	82.93	82.64	99.56	99.33	73.20	77.95	81.09	99.28	99.86
	文献［1］算法	56.37	63.27	58.15	96.11	76.04	54.05	64.69	58.83	95.33	81.30
	文献［26］算法	99.99	99.99	100.00	59.31	99.98	100.00	100.00	99.96	58.81	100.00
	文献［14］算法	98.65	99.01	99.86	57.83	99.84	98.37	98.69	99.48	58.85	99.73
	文献［32］算法	97.33	96.89	92.97	54.01	99.58	96.86	94.54	89.31	52.91	99.78
90	文献［43］算法	96.32	99.39	98.88	99.81	98.53	96.62	99.53	98.51	99.83	98.98
	文献［40］算法	62.14	66.09	68.85	93.51	99.01	64.64	65.96	69.27	92.58	99.27
	文献［1］算法	52.43	54.18	54.04	65.99	99.56	50.96	54.09	51.39	62.92	99.31
	文献［26］算法	97.69	99.81	98.98	99.92	76.03	97.57	99.98	99.75	100.00	76.45
	文献［14］算法	93.21	97.88	97.44	98.86	69.54	92.54	95.98	96.75	98.46	59.90
	文献［32］算法	97.78	97.63	94.23	97.49	53.22	94.34	95.28	94.65	91.51	58.66

Tab. 1 Performance comparison on UCID and NRCS datasets

$Q F 1$	算法	UCID数据集上的准确率/%					NRCS数据集上的准确率/%
$Q F 1$	算法	$Q F 2 = 50$	$Q F 2 = 60$	$Q F 2 = 70$	$Q F 2 = 80$	$Q F 2 = 90$	$Q F 2 = 50$	$Q F 2 = 60$	$Q F 2 = 70$	$Q F 2 = 80$	$Q F 2 = 90$
50	文献［43］算法	84.51	99.96	100.00	100.00	100.00	85.14	99.91	99.99	99.98	99.99
	文献［40］算法	87.17	89.01	96.86	99.54	99.70	86.94	87.79	96.58	99.69	99.81
	文献［1］算法	81.97	80.23	85.31	90.95	89.70	81.60	81.10	85.61	93.13	85.77
	文献［26］算法	51.61	100.00	99.95	99.99	100.00	50.82	99.99	99.99	100.00	100.00
	文献［14］算法	52.86	99.33	98.96	99.71	100.00	50.76	98.99	99.11	99.37	100.00
	文献［32］算法	50.33	92.57	94.95	98.33	99.84	51.45	96.79	95.16	97.67	99.26
60	文献［43］算法	99.77	84.42	100.00	99.99	99.99	99.59	84.7	99.99	99.98	99.98
	文献［40］算法	82.67	86.59	88.82	99.56	99.65	81.62	87.06	87.73	99.79	99.92
	文献［1］算法	68.31	82.18	81.50	81.40	86.07	62.10	81.40	82.07	89.72	82.28
	文献［26］算法	99.99	51.61	100.00	99.97	100.00	99.99	50.87	100.00	99.99	100.00
	文献［14］算法	98.53	50.06	98.71	99.97	99.99	98.48	50.79	97.63	99.48	100.00
	文献［32］算法	96.02	52.01	94.36	96.46	99.57	93.33	52.80	95.93	98.84	99.26
70	文献［43］算法	99.92	99.79	86.40	99.99	100.00	99.83	99.90	85.23	100.00	100.00
	文献［40］算法	84.25	82.73	88.37	99.31	98.92	85.17	82.87	89.28	99.46	99.13
	文献［1］算法	61.23	64.29	82.34	94.46	73.21	61.62	61.33	83.40	94.10	70.98
	文献［26］算法	99.99	99.99	51.22	99.99	100.00	100.00	100.00	50.89	99.97	100.00
	文献［14］算法	98.91	99.21	53.44	99.89	100.00	98.79	99.41	50.74	99.12	100.00
	文献［32］算法	95.12	90.83	51.04	98.9	99.91	90.88	89.53	50.26	97.25	99.54
80	文献［43］算法	99.57	99.91	99.99	98.43	99.99	99.60	99.93	99.97	98.56	99.99
	文献［40］算法	75.42	82.93	82.64	99.56	99.33	73.20	77.95	81.09	99.28	99.86
	文献［1］算法	56.37	63.27	58.15	96.11	76.04	54.05	64.69	58.83	95.33	81.30
	文献［26］算法	99.99	99.99	100.00	59.31	99.98	100.00	100.00	99.96	58.81	100.00
	文献［14］算法	98.65	99.01	99.86	57.83	99.84	98.37	98.69	99.48	58.85	99.73
	文献［32］算法	97.33	96.89	92.97	54.01	99.58	96.86	94.54	89.31	52.91	99.78
90	文献［43］算法	96.32	99.39	98.88	99.81	98.53	96.62	99.53	98.51	99.83	98.98
	文献［40］算法	62.14	66.09	68.85	93.51	99.01	64.64	65.96	69.27	92.58	99.27
	文献［1］算法	52.43	54.18	54.04	65.99	99.56	50.96	54.09	51.39	62.92	99.31
	文献［26］算法	97.69	99.81	98.98	99.92	76.03	97.57	99.98	99.75	100.00	76.45
	文献［14］算法	93.21	97.88	97.44	98.86	69.54	92.54	95.98	96.75	98.46	59.90
	文献［32］算法	97.78	97.63	94.23	97.49	53.22	94.34	95.28	94.65	91.51	58.66

Tab. 2 Grey box experimental results on UCID and NRCS datasets

$Q F 2$	UCID数据集上的准确率/%				NRCS数据集上的准确率/%
$Q F 2$	文献［43］算法	文献［40］算法	文献［26］算法	文献［1］算法	文献［43］算法	文献［40］算法	文献［26］算法	文献［1］算法
50	84.55	70.94	82.91	57.86	85.17	71.57	84.14	54.58
60	90.37	74.58	84.72	62.23	90.54	74.46	84.25	59.03
70	88.77	77.03	84.45	64.53	89.72	78.00	84.48	62.70
80	97.05	95.52	86.03	80.01	96.65	95.47	87.23	82.57
90	98.09	98.11	91.03	78.32	98.60	99.02	93.35	76.95

Tab. 2 Grey box experimental results on UCID and NRCS datasets

$Q F 2$	UCID数据集上的准确率/%				NRCS数据集上的准确率/%
$Q F 2$	文献［43］算法	文献［40］算法	文献［26］算法	文献［1］算法	文献［43］算法	文献［40］算法	文献［26］算法	文献［1］算法
50	84.55	70.94	82.91	57.86	85.17	71.57	84.14	54.58
60	90.37	74.58	84.72	62.23	90.54	74.46	84.25	59.03
70	88.77	77.03	84.45	64.53	89.72	78.00	84.48	62.70
80	97.05	95.52	86.03	80.01	96.65	95.47	87.23	82.57
90	98.09	98.11	91.03	78.32	98.60	99.02	93.35	76.95

Tab. 3 Triple compression forensic performance

$Q F$	单压缩和三重压缩的准确率/%			双压缩和三重压缩的准确率/%
$Q F$	文献［43］算法	文献［40］算法	文献［26］算法	文献［43］算法	文献［40］算法	文献［26］算法
50	88.79	82.95	80.82	67.36	63.85	53.24
60	92.56	84.82	86.88	72.08	66.86	54.38
70	93.48	86.33	88.51	69.89	65.31	52.83
80	99.00	97.44	93.28	74.21	70.85	58.91
90	99.34	98.93	97.48	71.69	70.23	65.89

Tab. 3 Triple compression forensic performance

$Q F$	单压缩和三重压缩的准确率/%			双压缩和三重压缩的准确率/%
$Q F$	文献［43］算法	文献［40］算法	文献［26］算法	文献［43］算法	文献［40］算法	文献［26］算法
50	88.79	82.95	80.82	67.36	63.85	53.24
60	92.56	84.82	86.88	72.08	66.86	54.38
70	93.48	86.33	88.51	69.89	65.31	52.83
80	99.00	97.44	93.28	74.21	70.85	58.91
90	99.34	98.93	97.48	71.69	70.23	65.89

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Review of JPEG recompression forensics for color images

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