基于辨识性统计特征的PQ隐密图像识别算法

doi:10.11959/j.issn.1000-436x.2015068

通信学报 ›› 2015, Vol. 36 ›› Issue (3): 139-148.doi: 10.11959/j.issn.1000-436x.2015068

基于辨识性统计特征的PQ隐密图像识别算法

卢记仓^1,³,刘粉林^2,³,罗向阳^2,³,张轶^2,⁴

¹ 解放军信息工程大学导航与空天目标工程学院，河南郑州 450001
² 解放军信息工程大学网络空间安全学院，河南郑州 450001
³ 数学工程与先进计算国家重点实验室，河南郑州 450001
⁴ 中国科学院信息工程研究所信息安全国家重点实验室，北京 100093

出版日期:2015-03-25 发布日期:2017-06-21
基金资助:
国家自然科学基金资助项目;国家自然科学基金资助项目;国家自然科学基金资助项目;中国博士后科学基金资助项目;中国博士后科学基金资助项目;信息保障技术重点实验室开放基金资助项目;河南省科技创新杰出青年基金资助项目;解放军信息工程大学优博基金资助项目

Recognition of PQ stego images based on identifiable statistical feature

Ji-cang LU^1,³,Fen-lin LIU^2,³,Xiang-yang LUO^2,³,Yi ZHANG^2,⁴

¹ School of Navigation and Aerospace Engineering, Zhengzhou Information Science and Technology Institute, Zhengzhou 450001, China
² School of Cyberspace Security, Zhengzhou Information Science and Technology Institute, Zhengzhou 450001, China
³ School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
⁴ State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China

Online:2015-03-25 Published:2017-06-21
Supported by:
The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Natural Science Foundation of China;China Postdoctoral Sci-ence Foundation;China Postdoctoral Sci-ence Foundation;Foundation of Science and Technology on Information Assurance Laboratory;The Excellent Youth Foundation of Henan Province of China;The Doctoral Dissertation Innovation Fund of Zhengzhou Information Science and Technology Institute

摘要/Abstract

摘要：

提出一种基于辨识性统计特征的PQ（perturbed quantization）隐密图像识别算法。该算法根据经典PQ隐写对图像数据的更改方式，提取可有效区分该类隐密图像与其他类隐密图像的辨识性统计特征，并运用SVM（support vector machines）分类器进行分类识别。实验结果表明，本算法能够可靠地将PQ隐密图像从5类典型JPEG隐写PQ、F5、nsF5、MB1和MOD的隐密图像中识别出来；即使F5、nsF5、MB1和MOD的隐密图像不参与分类器的训练，本算法仍能有效识别PQ隐密图像。

关键词: 隐写分析, 隐密图像识别, PQ隐写, 辨识性统计特征

Abstract:

A PQ (perturbed quantization) stego images recognition algorithm is proposed based on identifiable statistical feature. According to the specific changing ways of PQ steganography to image data, the proposed algorithm extracts the identifiable statistical feature that can distinguish PQ stego images from other types of stego images. Then, the SVM (support vector machines) classifier is trained to recognize PQ stego images. Experimental results show that, the proposed algorithm can reliably recognize PQ stego images from multi-class stego images generated by five types of well-known JPEG steganography (PQ、F5ns、F5、MB1 and MOD). Even though the stego images generated by F5、nsF5、MB1 and MOD are not used for training classifier, the proposed algorithm can still effectively recognize PQ stego images.

Key words: steganalysis, stego image recognition, PQ steganography, identifiable statistical feature

卢记仓,刘粉林,罗向阳,张轶. 基于辨识性统计特征的PQ隐密图像识别算法[J]. 通信学报, 2015, 36(3): 139-148.

Ji-cang LU,Fen-lin LIU,Xiang-yang LUO,Yi ZHANG. Recognition of PQ stego images based on identifiable statistical feature[J]. Journal on Communications, 2015, 36(3): 139-148.

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参考文献 23

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图像类别	信息嵌入比率/bpnc	图像数量
载体图像	0	5 000
PQ隐密图像	0.05、0.10、0.15、0.20	5 000×4 = 20 000
F5隐密图像	0.05、0.10、0.15、0.20	5 000×4 = 20 000
nsF5隐密图像	0.05、0.10、0.15、0.20	5 000×4 = 20 000
MB1隐密图像	0.05、0.10、0.15、0.20	5 000×4 = 20 000
MOD隐密图像	0.05、0.10、0.15、0.20	5 000×4 = 20 000

嵌入率/bpnc	PQ vs F5	PQ vs nsF5	PQ vs MB1	PQ vs MOD
0.05	92.47	91.65	92.27	91.87
0.10	97.67	96.93	97.65	97.15
0.15	98.27	97.23	98.51	97.66
0.20	98.55	97.22	98.81	97.94

嵌入率/bpnc	F5 vs nsF5	F5 vs MB1	F5 vs MOD	nsF5 vs MB1	nsF5 vs MOD	MB1 vs MOD
0.05	57.57	63.18	59.05	63.08	56.67	63.13
0.10	68.47	75.46	70.26	77.24	65.58	77.07
0.15	76.11	84.40	78.14	85.40	71.58	85.84
0.20	78.85	90.20	83.47	87.84	74.98	89.39

嵌入率/bpnc	检测算法	PQ	非PQ = (F5 + nsF5 + MB1 + MOD) / 4
	文献[5]算法	90.77	20.87 = (11.31 + 25.01 + 23.31 + 23.85) / 4
0.05	文献[9]算法	99.21	10.17 = (0.55 + 14.29 + 12.51 + 13.31) / 4
	本算法	90.03	6.59 = (5.09 + 7.44 + 6.68 + 7.16) / 4
	文献[5]算法	95.34	18.42 = (4.87 + 25.15 + 23.14 + 20.54) / 4
0.10	文献[9]算法	99.73	9.54 = (0.25 + 13.91 + 11.74 + 12.24) / 4
	本算法	98.52	5.52 = (3.19 + 7.18 + 5.64 + 6.08) / 4
	文献[5]算法	95.46	17.03 = (2.14 + 24.46 + 23.85 + 17.66) / 4
0.15	文献[9]算法	99.73	9.12 = (0.16 + 13.71 + 11.50 + 11.12) / 4
	本算法	98.52	5.52 = (3.19 + 7.18 + 5.64 + 6.08) / 4
	文献[5]算法	95.84	16.54 = (0.96 + 24.06 + 25.97 +15.58) / 4
0.20	文献[9]算法	99.78	8.95 = (0.10 + 13.50 + 11.93 + 10.25) / 4
	本算法	98.68	4.26 = (1.58 + 6.26 + 4.77 + 4.43) / 4

已知隐写算法		平均正确率			平均时间耗费			ROC曲线下面积
已知隐写算法	文献[5]算法	文献[9]算法	本算法	文献[5]算法	文献[9]算法	本算法	文献[5]算法	文献[9]算法	本算法
F5	84.28	92.37	95.08	38:42	31:23	09:49	0.962 2	0.988 5	0.991 6
nsF5	84.50	97.92	96.06	58:38	37:16	11:33	0.944 7	0.996 0	0.991 7
MB1	62.47	84.19	94.46	18:04	33:50	09:59	0.845 9	0.939 2	0.990 1
MOD	65.44	82.43	95.54	29:15	33:39	09:34	0.797 9	0.931 4	0.991 8

基于辨识性统计特征的PQ隐密图像识别算法

Recognition of PQ stego images based on identifiable statistical feature

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嵌入率/bpnc	检测算法	PQ	非PQ = (F5 + nsF5 + MB1 + MOD) / 4
	文献[5]算法	95.31	18.87 = (29.70 + 11.21 + 28.98 + 5.59) / 4
0.05	文献[9]算法	98.66	6.88 = (13.62 + 3.21 + 8.93 + 1.75) / 4
	本算法	93.09	8.96 = (8.29 + 9.02 + 9.66 + 8.85) / 4
	文献[5]算法	98.68	45.08 = (81.91 + 26.09 + 66.25 + 6.05) / 4
0.10	文献[9]算法	99.67	25.98 = (62.70 + 7.27 + 31.98 + 1.95) / 4
	本算法	99.19	9.33 = (8.09 + 9.30 + 11.13 + 8.78) / 4
	文献[5]算法	98.87	60.88 = (97.65 + 50.73 + 88.36 + 6.78) / 4
0.15	文献[9]算法	99.60	43.90 = (94.14 + 18.96 + 60.16 + 2.35) / 4
	本算法	99.29	9.99 = (8.42 + 9.66 + 13.01 + 8.88) / 4
	文献[5]算法	99.06	69.55 = (99.57 + 74.52 + 95.89 + 8.20) / 4
0.20	文献[9]算法	99.71	55.34 = (99.44 + 41.17 + 77.69 + 3.05) / 4
	本算法	99.26	10.83 = (8.95 + 10.04 + 15.55 + 8.79) / 4

检测算法	平均正确率	平均时间耗费	ROC曲线下面积
文献[5]算法	60.72	14:12	0.781 1
文献[9]算法	73.46	13:12	0.910 9
本算法	91.72	04:34	0.988 7