基于多示例學(xué)習圖卷積網(wǎng)絡(luò )的隱寫(xiě)者檢測

鐘圣華; 張智

doi:10.16383/j.aas.c220775

基于多示例學(xué)習圖卷積網(wǎng)絡(luò )的隱寫(xiě)者檢測

doi: 10.16383/j.aas.c220775

鐘圣華^1,,
張智^{1, 2,}

1.
深圳大學(xué)計算機與軟件學(xué)院深圳 518060
2.
香港理工大學(xué)電子計算學(xué)系香港 999077

基金項目: 廣東省自然科學(xué)基金(2023A1515012685, 2023A1515011296), 國家自然科學(xué)基金(62002230, 62032015)資助

詳細信息

作者簡(jiǎn)介:
鐘圣華：深圳大學(xué)計算機與軟件學(xué)院副教授. 主要研究方向為多媒體內容分析, 情感腦機接口. 本文通信作者. E-mail: csshzhong@szu.edu.cn

張智：深圳大學(xué)計算機與軟件學(xué)院研究助理, 香港理工大學(xué)電子計算學(xué)系博士研究生. 主要研究方向為隱寫(xiě)者檢測, 腦電信號分析. E-mail: zhi271.zhang@connect.polyu.hk

計量
- 文章訪(fǎng)問(wèn)數: 468
- HTML全文瀏覽量: 170
- PDF下載量: 138
- 被引次數: 0
出版歷程
- 收稿日期: 2022-09-30
- 錄用日期: 2023-04-12
- 網(wǎng)絡(luò )出版日期: 2023-08-09
- 刊出日期: 2024-04-26

Steganographer Detection via Multiple-instance Learning Graph Convolutional Networks

ZHONG Sheng-Hua^1
,,
ZHANG Zhi^{1, 2
,}

1.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060
2.
Department of Computing, The Hong Kong Polytechnic University, Hong Kong 999077

Funds: Supported by Natural Science Foundation of Guangdong Province (2023A1515012685, 2023A1515011296) and National Natural Science Foundation of China (62002230, 62032015)

More Information

Author Bio:
ZHONG Sheng-Hua　Associate professor at the College of Computer Science and Software Engineering, Shenzhen University. Her research interest covers multimedia content analysis and affective brain-machine interface. Corresponding author of this paper

ZHANG Zhi　Research assistant at the College of Computer Science and Software Engineering, Shenzhen University; Ph.D. candidate in the Department of Computing, The Hong Kong Polytechnic University. Her research interest covers steganographer detection and electroencephalography signal analysis

摘要

摘要: 隱寫(xiě)者檢測通過(guò)設計模型檢測在批量圖像中嵌入秘密信息進(jìn)行隱蔽通信的隱寫(xiě)者, 對解決非法使用隱寫(xiě)術(shù)的問(wèn)題具有重要意義. 本文提出一種基于多示例學(xué)習圖卷積網(wǎng)絡(luò ) (Multiple-instance learning graph convolutional network, MILGCN) 的隱寫(xiě)者檢測算法, 將隱寫(xiě)者檢測形式化為多示例學(xué)習(Multiple-instance learning, MIL) 任務(wù). 本文中設計的共性增強圖卷積網(wǎng)絡(luò )(Graph convolutional network, GCN) 和注意力圖讀出模塊能夠自適應地突出示例包中正示例的模式特征, 構建有區分度的示例包表征并進(jìn)行隱寫(xiě)者檢測. 實(shí)驗表明, 本文設計的模型能夠對抗多種批量隱寫(xiě)術(shù)和與之對應的策略.
- 圖像隱寫(xiě)者檢測 /
- 圖卷積網(wǎng)絡(luò ) /
- 多示例學(xué)習 /
- 示例包表征
Abstract: Steganographer detection aims to solve the problem of illegal use of batch steganography by designing models to detect steganographers who embed secret information in images for covert communication. This paper proposes a novel steganographer detection algorithm called as multiple-instance learning graph convolutional network (MILGCN) to formalize steganography detection as a multiple-instance learning (MIL) task. The commonness enhancement graph convolutional network (GCN) and attention graph readout module designed in this paper can adaptively highlight the positive instance pattern and construct distinguishable bag representations for steganographer detection. Experiments show that the designed model can successfully attack a variety of batch steganography and the corresponding strategies.
- Image steganograher detection /
- graph convolutional network (GCN) /
- multiple-instance learning (MIL) /
- bag of instances representation

HTML全文

圖 1 基于多示例學(xué)習圖卷積網(wǎng)絡(luò )的隱寫(xiě)者檢測框架

Fig. 1 Steganographer detection framework based on multiple-instance learning graph convolutional network

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圖 2 隱寫(xiě)者檢測框架中兩個(gè)模塊((a) 共性增強圖卷積模塊; (b) 注意力讀出模塊)

Fig. 2 Two modules in steganographer detection framework ((a) The commonness enhancement graph convolutional network module; (b) The attention readout module)

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圖 3 當測試階段隱寫(xiě)者使用不同隱寫(xiě)術(shù)、分享的載密圖像數量占總圖像數量的10%到100%時(shí), 不同的基于圖的隱寫(xiě)者檢測方法檢測準確率

Fig. 3 The accurate rate of different graph-based steganographer detection methods when the number of shared secret images is from 10% to 100% of the total number of images and the steganographer uses different steganography in test

下載: 全尺寸圖片幻燈片

圖 4 隱寫(xiě)者和正常用戶(hù)所對應圖結構的可視化

Fig. 4 Visualization of graph structures corresponding to steganographer and normal user

下載: 全尺寸圖片幻燈片

表 1 使用的變量符號及對應說(shuō)明

Table 1 The variable symbols and their corresponding descriptions

變量	含義
$B_x$	用戶(hù)$x$對應的示例包
$x_i$	示例包$B_x$內第$i$個(gè)示例
$m$	當前數據集中示例包的總數量
$n$	當前示例包中示例的總數量
$v_i$	共性增強圖卷積模塊的第$i$個(gè)輸入示例特征
$h_i$	對$v_i$使用$f$函數進(jìn)行特征提取后得到的示例特征向量
$H$	由示例特征向量構成的示例包的矩陣表示 $H=[h_1,\cdots,h_n]^{\rm{T}}$
$A_{ij}$	圖中第$i$個(gè)與第$j$個(gè)示例節點(diǎn)之間邊的權重
$N_i$	圖中第$i$個(gè)示例節點(diǎn)的所有鄰居節點(diǎn)
$A$	示例包$B_x$所構成圖的鄰接矩陣
$r_i$	進(jìn)行圖卷積后$h_i$所對應的示例特征向量
$R$	由示例特征向量構成的示例包的矩陣表示 $R=[r_1,\cdots,r_n]^{\rm{T}}$
$s_i$	進(jìn)行圖歸一化后$r_i$ 所對應的示例特征向量
$S$	由示例特征向量構成的示例包的矩陣表示 $S=[s_1,\cdots,s_n]^{\rm{T}}$
$t_i$	共性增強圖卷積模塊的第$i$個(gè)輸出示例特征
$T$	由示例特征向量構成的示例包的矩陣表示 $T=[t_1,\cdots,t_n]^{\rm{T}}$
$f$	特征提取函數
$g$	注意力計算函數
$z_i$	共性增強圖卷積模塊的輸出, 也是注意力讀出模塊的第$i$個(gè)輸入示例特征
$Z_x$	由共性增強圖卷積模塊得到的示例特征向量構成的用戶(hù)$x$對應的示例包的矩陣表示
$u_x$	用戶(hù)$x$對應的示例包的特征向量表征
$p_i$	當前示例包中第$i$個(gè)示例對示例包表征的貢獻
${\rho_i}$	第$i$個(gè)示例包的預測結果
$Y_i$	第$i$個(gè)示例包的真實(shí)標簽
$L$	本文設計的損失函數
$L_{\rm{bag}}$	本文設計的多示例學(xué)習分類(lèi)損失
$L_{\rm{entropy}}$	本文設計的熵正則損失
$L_{\rm{contrastive}}$	本文設計的對比學(xué)習損失
$\lambda_1, \lambda_2, \lambda_3$	超參數, 用于調整$L_{\rm{bag}},$ $L_{\rm{entropy}},$ $L_{\rm{contrastive}}$的權重

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表 2 已知隱寫(xiě)者使用相同圖像隱寫(xiě)術(shù)(S-UNIWARD) 時(shí)的隱寫(xiě)者檢測準確率(%), 嵌入率從0.05 bpp到0.4 bpp

Table 2 Steganography detection accuracy rate (%) when steganographers use the same image steganography (S-UNIWARD), while the embedding payload is from 0.05 bpp to 0.4 bpp

模型		嵌入率(bpp)
模型		0.05	0.1	0.2	0.3	0.4
前沿	MDNNSD	4	54	100	100	100
前沿	XuNet_SD	2	2	71	100	100
基于GAN	SSGAN_SD	0	1	1	2	4
基于GNN	GAT	2	3	3	3	4
	GraphSAGE	28	88	100	100	100
	AGNN	24	99	100	100	100
	GCN	19	96	100	100	100
	SAGCN	72	100	100	100	100
基于MIL	MILNN_self	15	87	100	100	100
基于MIL	MILNN_git	18	96	100	100	100
本文	MILGCN-MF	47	100	100	100	100
本文	MILGCN	74	100	100	100	100

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表 3 當測試階段隱寫(xiě)者使用相同隱寫(xiě)術(shù)(S-UNIWARD) 和分享的載密圖像數量占總圖像數量為10%到100%時(shí), SRNet-AVG和SRNet-MILGCN的檢測成功率 (%)

Table 3 The accurate rate (%) of SRNet-AVG and SRNet-MILGCN when the number of shared secret images is from 10% to 100% of the total number of images and the steganographer uses the same steganography (S-UNIWARD) in test

方法	占比(%)
方法	10	30	50	70	90	100
SRNet-AVG	26	100	100	100	100	100
SRNet-MILGCN	35	100	100	100	100	100

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表 4 當用戶(hù)分享不同數量的圖像時(shí), 使用MILGCN和SAGCN進(jìn)行隱寫(xiě)者檢測的準確率(%),嵌入率從0.05 bpp到0.4 bpp

Table 4 Steganography detection accuracy rate (%) of MILGCN and SAGCN when users share different numbers of images, while the embedding payload is from 0.05 bpp to 0.4 bpp

	數量(張)	嵌入率 (bpp)
	數量(張)	0.05	0.1	0.2	0.3	0.4
MILGCN	100	35	96	100	100	100
	200	74	100	100	100	100
	400	96	100	100	100	100
	600	100	100	100	100	100
SAGCN	100	31	96	100	100	100
	200	72	100	100	100	100
	400	91	100	100	100	100
	600	91	100	100	100	100

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表 5 在隱寫(xiě)術(shù)錯配情況下, 當分享的載密圖像數量占比5%時(shí), MILGCN取得的隱寫(xiě)者檢測準確率(%)

Table 5 Steganography detection accuracy rate (%) in the case of steganography mismatch when the number of shared secret images is 5% of the total number of images

測試隱寫(xiě)術(shù)	HUGO-BD	WOW	HILL	MiPOD
檢測準確率	6	4	5	5

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表 6 訓練模型使用HILL作為隱寫(xiě)術(shù), 分享的載密圖像數量占比10%或30%, MILGCN取得的隱寫(xiě)者檢測準確率(%)

Table 6 Steganography detection accuracy rate (%) when the steganography used for training is HILL and the number of shared secret images is 10% or 30% of the total number of images

載密圖像比例	測試隱寫(xiě)術(shù)
載密圖像比例	HUGO-BD	WOW	HILL	MiPOD
10%	9	6	7	4
30%	37	48	49	47

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表 7 已知隱寫(xiě)者使用相同圖像隱寫(xiě)術(shù)(J-UNIWARD)時(shí)的隱寫(xiě)者檢測準確率(%), 嵌入率從0.05 bpnzAC到0.4 bpnzAC

Table 7 Steganography detection accuracy rate (%) when steganographer use the same image steganography (J-UNIWARD) and the embedding payload is from 0.05 bpnzAC to 0.4 bpnzAC

模型	嵌入率(bpnzAC)
模型	0.05	0.1	0.2	0.3	0.4
JRM_SD	11	17	25	31	48
PEV_SD	0	0	1	1	5
GraphSAGE	13	68	100	100	100
AGNN	13	84	100	100	100
GCN	16	88	100	100	100
SAGCN	17	92	100	100	100
MILGCN	25	92	100	100	100

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表 8 當測試階段隱寫(xiě)者使用nsF5或UERD等圖像隱寫(xiě)術(shù)嵌入秘密信息時(shí), 不同方法的隱寫(xiě)者檢測準確率(%),嵌入率從0.05 bpnzAC到0.4 bpnzAC

Table 8 Steganography detection accurate rate (%) of different methods when steganographer uses nsF5 or UERD as image steganography in the testing phase and the embedding payload is from 0.05 bpnzAC to 0.4 bpnzAC

隱寫(xiě)術(shù)	模型	嵌入率(bpnzAC)
隱寫(xiě)術(shù)	模型	0.05	0.1	0.2	0.3	0.4
nsF5	PEV_SD	0	1	9	52	93
	GraphSAGE	21	91	100	100	100
	AGNN	20	90	100	100	100
	GCN	24	90	100	100	100
	SAGCN	29	92	100	100	100
	MILGCN	22	90	100	100	100
UERD	GraphSAGE	25	91	100	100	100
	AGNN	29	94	100	100	100
	GCN	33	96	100	100	100
	SAGCN	33	98	100	100	100
	MILGCN	42	99	100	100	100

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表 9 計算復雜度分析

Table 9 The analysis of computational complexity

方法名稱(chēng)	批次平均運行時(shí)間(s)	單個(gè)樣本浮點(diǎn) 運算數(千兆次)	參數量(千個(gè))
MILNN	0.001	0.003	12.92
GCN	0.830	2.480	67.97
SAGCN	2.210	7.410	67.94
MILGCN	0.020	0.070	74.18

下載: 導出CSV

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