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              基于多示例學習圖卷積網絡的隱寫者檢測

              鐘圣華 張智

              鐘圣華, 張智. 基于多示例學習圖卷積網絡的隱寫者檢測. 自動化學報, 2024, 50(4): 771?789 doi: 10.16383/j.aas.c220775
              引用本文: 鐘圣華, 張智. 基于多示例學習圖卷積網絡的隱寫者檢測. 自動化學報, 2024, 50(4): 771?789 doi: 10.16383/j.aas.c220775
              Zhong Sheng-Hua, Zhang Zhi. Steganographer detection via multiple-instance learning graph convolutional networks. Acta Automatica Sinica, 2024, 50(4): 771?789 doi: 10.16383/j.aas.c220775
              Citation: Zhong Sheng-Hua, Zhang Zhi. Steganographer detection via multiple-instance learning graph convolutional networks. Acta Automatica Sinica, 2024, 50(4): 771?789 doi: 10.16383/j.aas.c220775

              基于多示例學習圖卷積網絡的隱寫者檢測

              doi: 10.16383/j.aas.c220775
              基金項目: 廣東省自然科學基金(2023A1515012685, 2023A1515011296), 國家自然科學基金(62002230, 62032015)資助
              詳細信息
                作者簡介:

                鐘圣華:深圳大學計算機與軟件學院副教授. 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:深圳大學計算機與軟件學院研究助理, 香港理工大學電子計算學系博士研究生. 主要研究方向為隱寫者檢測, 腦電信號分析. E-mail: zhi271.zhang@connect.polyu.hk

              Steganographer Detection via Multiple-instance Learning Graph Convolutional Networks

              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: 隱寫者檢測通過設計模型檢測在批量圖像中嵌入秘密信息進行隱蔽通信的隱寫者, 對解決非法使用隱寫術的問題具有重要意義. 本文提出一種基于多示例學習圖卷積網絡 (Multiple-instance learning graph convolutional network, MILGCN) 的隱寫者檢測算法, 將隱寫者檢測形式化為多示例學習(Multiple-instance learning, MIL) 任務. 本文中設計的共性增強圖卷積網絡(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行隱寫者檢測. 實驗表明, 本文設計的模型能夠對抗多種批量隱寫術和與之對應的策略.
              • 圖  1  基于多示例學習圖卷積網絡的隱寫者檢測框架

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

                圖  2  隱寫者檢測框架中兩個模塊((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)

                圖  3  當測試階段隱寫者使用不同隱寫術、分享的載密圖像數量占總圖像數量的10%到100%時, 不同的基于圖的隱寫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

                圖  4  隱寫者和正常用戶所對應圖結構的可視化

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

                表  1  使用的變量符號及對應說明

                Table  1  The variable symbols and their corresponding descriptions

                變量含義
                $B_x$用戶$x$對應的示例包
                $x_i$示例包$B_x$內第$i$個示例
                $m$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$i$個輸入示例特征
                $h_i$對$v_i$使用$f$函數進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$i$個與第$j$個示例節點之間邊的權重
                $N_i$圖中第$i$個示例節點的所有鄰居節點
                $A$示例包$B_x$所構成圖的鄰接矩陣
                $r_i$進行圖卷積后$h_i$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$x$對應的示例包的矩陣表示
                $u_x$用戶$x$對應的示例包的特征向量表征
                $p_i$當前示例包中第$i$個示例對示例包表征的貢獻
                ${\rho_i}$第$i$個示例包的預測結果
                $Y_i$ 第$i$個示例包的真實標簽
                $L$本文設計的損失函數
                $L_{\rm{bag}}$本文設計的多示例學習分類損失
                $L_{\rm{entropy}}$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
                下載: 導出CSV

                表  2  已知隱寫者使用相同圖像隱寫術(S-UNIWARD) 時的隱寫者檢測準確率(%), 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)
                0.050.10.20.30.4
                前沿MDNNSD454100100100
                XuNet_SD2271100100
                基于GANSSGAN_SD01124
                基于GNNGAT23334
                GraphSAGE2888100100100
                AGNN2499100100100
                GCN1996100100100
                SAGCN72100100100100
                基于MILMILNN_self1587100100100
                MILNN_git1896100100100
                本文MILGCN-MF47100100100100
                MILGCN74100100100100
                下載: 導出CSV

                表  3  當測試階段隱寫者使用相同隱寫術(S-UNIWARD) 和分享的載密圖像數量占總圖像數量為10%到100%時, 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(%)
                1030507090100
                SRNet-AVG26100100100100100
                SRNet-MILGCN35100100100100100
                下載: 導出CSV

                表  4  當用戶分享不同數量的圖像時, 使用MILGCN和SAGCN進行隱寫者檢測的準確率(%),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)
                0.050.10.20.30.4
                MILGCN1003596100100100
                20074100100100100
                40096100100100100
                600100100100100100
                SAGCN1003196100100100
                20072100100100100
                40091100100100100
                60091100100100100
                下載: 導出CSV

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

                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

                測試隱寫術HUGO-BDWOWHILLMiPOD
                檢測準確率6455
                下載: 導出CSV

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

                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術
                HUGO-BDWOWHILLMiPOD
                10%9674
                30%37484947
                下載: 導出CSV

                表  7  已知隱寫者使用相同圖像隱寫術(J-UNIWARD)時的隱寫者檢測準確率(%), 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)
                0.050.10.20.30.4
                JRM_SD1117253148
                PEV_SD00115
                GraphSAGE1368100100100
                AGNN1384100100100
                GCN1688100100100
                SAGCN1792100100100
                MILGCN2592100100100
                下載: 導出CSV

                表  8  當測試階段隱寫者使用nsF5或UERD等圖像隱寫術嵌入秘密信息時, 不同方法的隱寫者檢測準確率(%),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

                隱寫術模型嵌入率(bpnzAC)
                0.050.10.20.30.4
                nsF5PEV_SD0195293
                GraphSAGE2191100100100
                AGNN2090100100100
                GCN2490100100100
                SAGCN2992100100100
                MILGCN2290100100100
                UERDGraphSAGE2591100100100
                AGNN2994100100100
                GCN3396100100100
                SAGCN3398100100100
                MILGCN4299100100100
                下載: 導出CSV

                表  9  計算復雜度分析

                Table  9  The analysis of computational complexity

                方法名稱批次平均
                運行時間(s)
                單個樣本浮點
                運算數(千兆次)
                參數量(千個)
                MILNN0.0010.00312.92
                GCN0.8302.48067.97
                SAGCN2.2107.41067.94
                MILGCN0.0200.07074.18
                下載: 導出CSV
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                        出版歷程
                        • 收稿日期:  2022-09-30
                        • 錄用日期:  2023-04-12
                        • 網絡出版日期:  2023-08-09
                        • 刊出日期:  2024-04-26

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