A Fusion Steganographic Algorithm Based on Faster R-CNN

Transcription

1 Copyrght 2018 Tech Scence Press CMC, vol.55, no.1, pp.1-16, 2018 A Fuson Steganographc Algorthm Based on Faster R-CNN Ruohan Meng 1, 2, Steven G. Rce 3, Jn Wang 4 1, 2, * and Xngmng Sun Abstract: The am of nformaton hdng s to embed the secret message n a normal cover meda such as mage, vdeo, voce or text, and then the secret message s transmtted through the transmsson of the cover meda. The secret message should not be damaged on the process of the cover meda. In order to ensure the nvsblty of secret message, complex texture objects should be chosen for embeddng nformaton. In ths paper, an approach whch corresponds multple steganographc algorthms to complex texture objects was presented for hdng secret message. Frstly, complex texture regons are selected based on a knd of objects detecton algorthm. Secondly, three dfferent steganographc methods were used to hde secret message nto the selected block regon. Expermental results show that the approach enhances the securty and robustness. Keywords: Faster R-CNN, fuson steganography, object detecton, CNNs, nformaton hdng. 1 Introducton Wth the rapd development of the nternet and nformaton technology, how to protect the transmsson of mportant nformaton has ganed a lot of attentons. Informaton hdng s an approach of puttng the secret nformaton nto a carrer (such as dgtal mages) from sender to recever. The recever extracts and restores the orgnal embedded secret nformaton through a specfc method. The carrer that carryng secret nformaton s called cover, whch has certanly sgnfcance n tself. For example, t can be an mage, a document, etc. The carrer adds secret nformaton called stego. In the deal stuaton, stego does not arouse suspcon by attacker n the dssemnaton process. Accordng to the dfferent use of nformaton hdng, t s dvded nto steganography and dgtal watermarkng, the former s manly used for the transmsson of secret nformaton, and the latter s manly used for the protecton of ntellectual property. Accordng to the technology of nformaton hdng, t can be dvded nto a varety of steganographc modes n spatal doman, transform doman and compressed doman. In the early days, the 1 School of Computer and Software, Nanjng Unversty of Informaton Scence and Technology, Nng Lu Road, No. 219, Nanjng, , Chna. 2 Jangsu Engneerng Centre of Network Montorng, Nng Lu Road, No. 219, Nanjng, , Chna. 3 Department of Mathematcs and Computer Scence, Northeastern State Unversty Tahlequah, OK 74464, USA. 4 School of Computer & Communcaton Engneerng, Changsha Unversty of Scence & Technology, Changsha, , Chna. * Correspondng author: Xngmng Sun. Emal: sunnudt@163.com. CMC. do: /cmc

2 2 Copyrght 2018 Tech Scence Press CMC, vol.55, no.1, pp.1-16, 2018 smplest and most representatve method of nformaton hdng n space doman s to hde the nformaton by usng the least sgnfcant bt (LSB) of the mage or all bt algorthms of multple bt planes. However, these algorthms are not robust enough to keep statstcal characterstcs, so that attackers can accurately estmate the embeddng length accordng to the statstcal detecton methods. The typcal detecton algorthms are RS (regular and sngular) groups method [Frdrch and Goljan (2002)], SPA (sample par analyss) method [Dumtrescu, Wu and Wang (2003)] and JPEG compatblty analyss [Frdrch, Goljan and Du (2001)] and so on. Wth the contnuous development of steganography, the newly proposed steganography algorthm can mantan more complex mage statstcal features. For example, HUGO [Pevný, Fller and Bas (2010)], WOW [Holub and Frdrch (2012)], SUNIWARD [Holub, Frdrch and Denemark (2014)] and other content adaptve steganographc algorthms proposed n recent years can automatcally embed the secret nformaton nto the rch nosy texture area on the cover mage to mantan hgh-level statstcs. Deep learnng s well known as a revoluton n machne learnng, especally n the feld of computer vson. In tradtonal approaches of mage feature extracton, the features of SIFT and BOW are usually used as representaton of mages. In recent years, the applcatons of these features extracton nclude mage retreval, mage forenscs and other prvacy protecton felds. Xa et al. [Xa, Zhu, Sun et al. (2018); Xa, Xong, Vaslakos et al. (2017)] apply mage retreval to cloud computng. Zhou et al. [Zhou, Yang, Chen et al. (2016); Zhou, Wu, Huang et al. (2017); Zhou, Wu, Yang et al. (2017); Zhou, Wang, Wu et al. (2017); Cao, Zhou, Sun et al. (2018); Zhou, Mu and Wu (2018)] proposed to apply the tradtonal features of the mage to coverless nformaton hdng. Yuan et al. [ Yuan, L, Wu et al. (2017)] use CNN to detect fngerprnt lveness. However, the appearance of mage depth learnng features makes the feature extracton more rapd and accurate. In the feld of mage classfcaton, deep convolutonal neural networks (DCNN) such as VGGNet [Smonyan and Zsserman (2014)], GoogLeNet [Szegedy, Lu, Ja et al. (2015)] and AlexNet [Krzhevsky, Sutskever and Hnton (2012)] are performance excellent. Gurusamy et al. [Gurusamy and Subramanam (2017)] used a machne learnng approach for MRI Bran Tumor Classfcaton. Based on the achevements above, object detecton has been rapdly developed that detects semantc objects of a class (such as a dog, vehcle, or person) n dgtal mages and vdeo. For texture-rch target mages, the appearance features of object nstances are manly dentfed and detected by extractng stable and abundant feature ponts and correspondng feature descrptors. In deep learnng fles, Faster R-CNN [Ren, Grshck, Grshck et al. (2017)], R-FCN [Da, L, He et al. (2016)] and SSD [Lu, Anguelov, Erhan et al. (2016)] three object detecton models that most wdely used currently. Amng at nformaton hdng, steganalyss approaches based on deep learnng has appeared one after another. These methods have a very good test for the current steganography algorthm. For example, Ye et al. [Ye, N and Y (2017)] proposed to mprove the CNN model to detect hdden nformaton has reached 99 % of the detecton rate. At the same tme, some researchers turned ther attenton to how to hde nformaton based on deep learnng, whch makes t safer and more robust. Tang et al. [Tang, Tan, L et al. (2017)] use generatve adversaral network (GAN) to acheve end-to-end nformaton hdng. Baluja [Baluja (2017)] use neural network to determne the embeddng secret

3 A Fuson Steganographc Algorthm Based on Faster R-CNN 3 nformaton n the locaton of the mage, tran an encoder to embed nformaton. Uchda et al. [Uchda, Naga, Sakazawa et al. (2017)] embed watermark nto the depth neural network model. Therefore, the combnaton of deep learnng and nformaton hdng has become the focus of ths paper. The exstng methods usually adopt a hdden mode to hde the secret nformaton or watermarkng n the entre mage [Xa, Wang, Zhang et al. (2016); Wang, Lan and Sh (2017); Chen, Zhou, Jeon et al. (2017)]. In order to enhance the securty and complexty of nformaton, t s straghtforward to desgn a fuson hdng strategy that employs dfferent steganography algorthms to hde nformaton on dfferent areas. In addton, the complexty of the mage s closely related to human vsual effects. The more complex the mage, the more nformaton t carres, but the vsual nformaton of people does not ncrease wth the ncrease of mage complexty. Usng the effect of human vsual redundancy, the nformaton s hdden n the more complcated area of the mage texture, so as to ncrease the robustness and ant-detectablty of hdng process. Therefore, ths work proposes an approach to detect the more complex area to hde the nformaton by the method of object detecton. We adopt the fuson method of multple steganographc algorthms as multple steganography Algorthms based on ROI (MSA_ROI) to hde the nformaton. 2 Related works 2.1 Object detecton The am of object detecton s to fnd the locaton of all the targets and specfy each target category on a gven mage or vdeo. It s manly dvded nto two tasks, target postonng and target category detecton. In tradtonal methods, the object detecton s manly use sldng wndow framework. The common algorthm s DPM (Deformable Part Model) [Felzenszwalb, Grshck, Mcallester et al. (2010)]. In face detecton, pedestran detecton and other tasks, DPM have acheved good results. But DPM s relatvely complex that the detecton speed s relatvely slow. Wth the development of deep learnng, object detecton has entered a new era. Object detecton methods related to deep learnng can be dvded nto two categores, one s based on the regonal nomnaton, such as R-CNN (Regonbased Convolutonal Neural Networks) [Grshck, Donahue, Darrell et al. (2014); He, Zhang, Ren et al. (2015)], SPP-net [He, Zhang, Ren et al. (2015)], Fast R-CNN [Grshck (2015)], Faster R-CNN, etc. The other s end-to-end approach, Such as YOLO [Redmon, Dvvala, Grshck et al. (2016)] and SSD. At present, Faster R-CNN model s a typcal object detecton model based on deep learnng. From R-CNN, Fast R-CNN to Faster R- CNN whch used n ths paper, the four basc steps of object detecton (regon proposal, feature extracton, classfcaton and rectangles refne regresson) are fnally unfed nto a deep network framework to acheve end-to-end object detecton. 2.2 Spatal doman steganography The current steganography methods are manly dvded nto two types: spatal doman and transform doman. In transform doman, man applcaton s JPEG. JPEG s a type of mage compresson method that dvdes an mage nto several matrces, performs a dscrete cosne varaton on each matrx and transforms the specfc pxel values on the mage completely

4 4 Copyrght 2018 Tech Scence Press CMC, vol.55, no.1, pp.1-16, 2018 nto the frequency doman. Snce human eye s senstve to low frequency but nsenstve to hgh frequency, all the hgh frequency nformaton s elmnated so as to acheve the purpose of mage compresson. Hdden nformaton wll be embedded n the ntermedate frequency area (hgh-frequency area of ant-attack, low-frequency changes n the regon s easy to be perceved by the human eye), whch wll be embedded nformaton dspersed throughout the mage. In spatal doman, embeddng nformaton s changng pxel values drectly. Adaptve steganography s to automatcally select the carrer mage whch s not easly found by the attacker based on the content of the cover mage features on the regon of nterest and embed secret nformaton. Modfyng pxels of the mage causes less dstorton to the mage on the complexty of the rch texture area and the edge area. At the same tme, t s dffcult for the attacker to detect secret nformaton. The steganography that based on the prncple of mnmzng the embedded dstorton cannot only ensure the mnmum dstorton rate but also acheves secret communcaton. Exstng steganography methods that based on the prncple of mnmzng embedded dstorton nclude: HUGO, WOW, S-UNIWARD, MVGG [L, Wang, Huang et al. (2015)] and so on. The ultmate goal s the same: mnmze the dstorton functon and embed t n nosy or complex textures regon of the cover mage S-UNIWARD algorthm S-UNIWARD s a content adaptve steganography based on wavelet transform, whch proposes a general dstorton functon ndependent of the embedded doman. The embeddng dstorton functon of S-UNIWARD as a whole s: D( X, Y) ( ) ( ) 3 n1 n k k 2 Wuv ( X ) Wuv ( Y) (1) W ( X) ( k ) k 1 u1 v1 uv (1) (2) (3) Here, K, K, K represent horzontal, vertcal, dagonal drecton of the flter n three (1) (2) (3) drectons, calculated by K h g T, K g h T, K g g T, where h represents a onedmensonal wavelet decomposton low (hgh) pass flter. Parameter X represents the carrer mage, the mage sze s n 1 n 2, and parameter Y represents the mage after ( k) ( k) embeddng the message. Parameter Wuv ( X ), Wuv ( Y) represent the mage of the carrer mage and the encrypted mage after wavelet transform. Formula (2) s used to calculate the wavelet coeffcents of pxels n three drectons of the orgnal mage. W K * X,(1 u n,1 v n,1 k 3) (2) ( k) ( k) uv uv 1 2 When the wavelet coeffcents of pxels (such as texture regons) wth complex content areas are changed, the dstorton calculated by formula (1) wll be small, ndcatng that the regon s sutable for hdng nformaton. However, when the pxel wavelet coeffcents of the texture smoothng regon are changed, the dstorton wll be very large, ndcatng that secret nformaton should be avoded when embeddng these pxels HUGO algorthm HUGO s consdered to be one of the most secure steganographc technques. It defnes a dstorton functon doman by assgnng costs to pxels based on the effect of embeddng some nformaton wthn a pxel, the space of pxels s condensed nto a feature space usng

5 A Fuson Steganographc Algorthm Based on Faster R-CNN 5 a weghted norm functon. HUGO algorthm s the use of SPAM steganalyss feature desgn dstorton cost functon. It s consdered to be one of the most secure steganographc technques. Accordng to the addtve dstorton functon: n D( X, Y ) x y (3) 1 Here, the constant 0 s a fxed parameter that represents the amount of dstorton that results from a pxel change. When, the pxel s the so-called wet pxel, and the wet pxel does not allow modfcaton durng embeddng. The mnmum expected dstorton functon s: D ( m, n, p) p (4) mn n 1 e Where p s the probablty that the -th pxel changes. Parameter 1 e n ( ) 1,(0 ) s the set of addtve dstorton metrcs formula (3), where {1,..., n}. Parameter m(0 m n) s the number of bts to be passed when usng bnary embeddng operatons WOW algorthm WOW (weght acquston wavelet) s another method of steganography, whch dependng on the complexty of the regon. It wll be covered embeddng nformaton nto an mage. If one area of the mage s more complex than the other, the pxel values n that area wll be modfed. WOW steganography algorthm manly from the perspectve desgn of the dstorton functon. The addtve dstorton functon s: n1 n2 D( X, Y) ( X, Y ) X Y 1 j1 j j j j Y. X Where j are the costs of changng pxel j to j 2.3 Qualty assessment The expermental results n ths paper used the followng ndexes to evaluate the qualty of stego mages Mean Square Error (MSE) The followng expresson s the Mean Square Error (MSE) between the mages AI and BI. Supposng that the pxel value of the bearer mage s AI (, j),0 j N 1 and the pxel value of the correspondng stego mage s BI (, j),0 M 1,0 j N 1, the error mage s e(, j) AI (, j) BI (, j),0 M 1,0 j N 1, then the mean square error s expressed as: (5)

6 6 Copyrght 2018 Tech Scence Press CMC, vol.55, no.1, pp.1-16, 2018 M 1 N 1 M 1 N 1 MSE 1 e j 1 MN MN AI j BI j 2 2, (, ) (, ) (6) 0 j 0 0 j 0 Lower s the MSE, hgher s the smlarty between the mages Peak Sgnal to Nose Rato (PSNR) The followng expresson s the Peak Sgnal to Nose Rato (PSNR) between the mages k AI and BI. Settng AImax 2 1, where K represents a number of bts for all pxels, the PSNR s defned as: 2 2 AI max AImax MN PSNR 101g 101g M 1N 1 db MSE 2 AI(, j) BI(, j) 0 j0 In many vdeo sequences and commercal mage acquston applcatons, often take k = 8. So, for an 8-bt bnary mage, AImax 255, substtutng formula (7) nto: (7) MN PSNR 101g 101 g ( db) M1N1 MSE 2 AI(, j) BI(, j) 0 j0 Hgher s the PSNR, hgher s the smlarty between the mages. (8) Structural Smlarty Index Measure (SSIM) The Structure Smlarty Index Measure (SSIM) between the mages OI and WI whch are of sze M N s gven by the followng expresson. Gven two mages AI and BI, the structural smlarty of the two mages can be found n accordance wth the followng formula: (2 c )(2 c ) SSIM ( AI, BI ) ( )( ) AI BI 1 AIBI AI BI c1 AI BI c2 2 2 Here, AI a s the average of AI, BI s the average of BI, s the varance of AI, AI s BI 2 2 the varance of BI, and AIBI s the covarance of AI and BI. c1 ( k1l), c2 ( k2l) are constants used to mantan stablty. L s the dynamc range of pxel values. k1 0.01, k Structural smlartes range from -1 to 1. When two mages are dentcal, the value of SSIM equals one. 3 Proposed method usng multple algorthms based on Faster R-CNN Ths secton dscusses our steganographc scheme, the models we use and the nformaton each party wshes to conceal or reveal. After layng ths theoretcal groundwork, we present experments supportng our clams. The overall framework for ths artcle s shown n Fg. 1. Frstly, the whole mage s nput nto the CNN model, and then the feature s extracted. Then, the Proposal s generated through the RPN network, the Proposal s mapped to the last layer of convoluton of CNN, and each proposal s made nto a fxed-sze feature maps (9)

7 A Fuson Steganographc Algorthm Based on Faster R-CNN 7 through Rol poolng layer. Fnally, use Softmax classfcaton and boundng box regresson to get the part of the carrer we need. Next, the steganographc algorthms are matched for each of the obtaned carrer parts to fnally obtan stego mages. 3.1 Extract object by Faster R-CNN In the feld of computer vson, object detecton manly solves two problems: the locaton of multple objects on the mage and the categores of each object. Faster R-CNN ntroduced the regon proposal network (RPN) based on Fast R-CNN, replacng the slow search selectve search algorthm. Regon proposal uses nformaton such as the texture, edge, and color n the mage to fnd out the poston where the target on the way may appear beforehand, and can guarantee a hgher recall rate wth fewer wndows selected (a few hundred or even a few thousand). Ths greatly reduces the tme complexty of follow-up operatons, and obtans the canddate wndow than the sldng wndow of hgher qualty. In a sense, Faster R-CNN = RPN + Fast R-CNN. Takng nto account the target detecton s based on the mage texture, edge and determne the target. Ths paper argues that objects selected from the Faster R-CNN are more conducve to hdng nformaton than the background. Therefore, Faster R-CNN s used n the method proposed here. Faster R- CNN's network model s shown n Fg. 2. Faster R-CNN s manly dvded nto four contents: Extract object by Faster R-CNN (a) Proposals... Feature Maps Proposals... Input Image Feature Extracton Regon Proposal Network Rol poolng Classfer Boundng box of Steganographc Algorthm (b) Regons of Objects Matchng Algorthm Pretreatment Fgure 1: Proposed Steganographc archtecture. (a) Target detecton structure based on Faster R-CNN. (b) Steganography algorthm structure for the local area matchng Conv layers: Includng the 13 conv layers +13 relu layers +4 poolng layers, used to extract mage features maps. The feature maps are shared for subsequent RPN layers and full connectvty layers. Regon Proposal Networks: used to generate regon proposals. The RPN network frst passes a 33 convolutonal layer, generatng foreground anchors and boundng box

8 8 Copyrght 2018 Tech Scence Press CMC, vol.55, no.1, pp.1-16, 2018 regresson offsets, respectvely, and then calculates proposals. Anchors belong to foreground or background va softmax functon. Ro Poolng: Ths layer accordng to feature maps and proposals to extract proposal feature maps, nto the subsequent full connecton layer. Classfcaton: Use proposal feature maps to calculate the type of proposal, and then use boundng box regresson to get the fnal exact poston of the test box. Conv layers(13), Relu layers(13), Poolng layers(4) Input Image... Feature Map ROIPoolng Proposal Reshape Softmax Reshape 1x1 3x3 1x1 Bbox_pred Softmax cls_prob Fgure 2: Network model of Faster R-CNN, ncludng convolutonal layers, regon proposal network, RoI poolng and classfcaton The loss functon of Faster R-CNN s: 1 1 L p t L p p p L t t (10) * * * ({ },{ }) cls (, ) reg (, ) Ncls Nreg p s the predcted probablty of anchor beng an object. The ground-truth label: p * 0 1 t t, t, t, t x y w h negatvelable postve lable * box of the predcton. t (11) s a vector representng the 4 parameterzed coordnates of the boundng s the coordnate vector of the ground truth boundng box correspondng to the postve anchor. * cls, * * * Lcls p, p log[ p p (1 p )(1 p )] * Lreg t, t s the regresson loss, calculated usng L t t s the logarthmc loss target and non-target: (12) L t t R t t, where R s the * * reg, ( ) * * smooth L1 functon. pl means that only the foreground anchor( p 1) has a regresson reg * loss, and n other cases there s no ( p 0). The outputs of cls and reg are composed of p andu, respectvely, then normalzed by Ncls and N reg.

9 A Fuson Steganographc Algorthm Based on Faster R-CNN Matchng process of steganographc algorthm From the frst part, we get multple texture complex regons. Next, we need to hde the nformaton by matchng the regons wth dfferent steganographc algorthms as shown n Fg. 3. Frst of all, the proposed method needs to judge whether there s overlap n the target area. If there are overlappng parts, the overlap part should be processed. Then the mage should be grayscale. In the order stage, the hash algorthm s used to sort the target area n each cover mage. Fnally, the sorted target area and the steganography algorthm s sequentally matched to complete the concealment of the secret nformaton. Selecton S-UNIWARD Regons of Objects Grayscale... WOW Stego HUGO Judgement Pretreament Order Matchng Fgure 3: Flow chart of steganographc algorthm matchng process Selecton of overlapped box Frst of all, many of the target frames are overlapped after a target s detected by Faster R- CNN. Secondly, only one steganographc method can be used to hde the nformaton n each target frame. Therefore, t needs to preprocess the overlapped box. As shown n Fg. 4, we take the prncple of maxmum area, for overlappng target box, frst calculate the area of overlapped box, wth a large target box shall preval, small target box by removng the remanng large area as a carrer. (a) (b) (c) (d) Fgure 4: Preprocess for overlappng target box. (a) Target area obtaned by Faster R-CNN. From the graph, we can see that there are overlappng areas n the red border. If the correspondng steganography algorthm s based on the box, t wll cause the overlappng area to hde the nformaton repeatedly. (b) Removng the overlapped area to get the green area. Due to the prncple of choosng maxmum area. (c), the result s shown n the purple area on the choce of maxmum area. The blue and green areas n (d) are the areas of the fnal choce Order of probablty scores

10 10 Copyrght 2018 Tech Scence Press CMC, vol.55, no.1, pp.1-16, 2018 After obtanng the grayscale regonal target maps, we apply dfferent steganographc algorthms to dfferent regons for embeddng nformaton. Each target graph obtaned by Faster R-CNN has a probablty value of Softmax functon. We sort dfferent regons based on ths value. As shown n Fg. 5, there are three boxes n the fgure, from left to rght named box 1, box 2, box 3. The probablty scores of each box are 0.995, 0.968, and Accordng to descendng order, the probablty scores are 0.995, 0.994, and The sorted areas correspond to the boxes 1, 3, and 2, respectvely numbered as 1, 2, and 3. Fgure 5: Target areas of the cover mage. For dfferent regons have dfferent probablty values, from left to rght are 0.995, , Match steganographc algorthms After gettng the order of probablty scores, we use hash algorthm to match steganographc algorthms. The method s usng dvson hash algorthm on the number, takng the remander of 3. When the remander s 0, S-UNIWARD algorthm s used. When the remander s 1, WOW algorthm s used and when the remander s 2, we use the HUGO algorthm. As shown n Fg. 6, the stego mage s used by dfferent matchng steganographc algorthms to embed the nformaton n dfferent regons. Fgure 6: The stego mage 4 Experments All the experments GPU envronment s NVIDIA GTX1080. The experment tranng data set s COCO2014 dataset [Ln, Mare, Belonge et al. (2014)], and the target n the mage s calbrated by exact segmentaton. The mage ncludes 91 categores of targets, 328,000 mages and 2,500,000 labels. All experments used the depth learnng framework Caffe [Ja, Shelhamer, Jeff et al. (2014)]. The network used by the mage feature extracton secton when tranng Faster R-CNN s VGG16 [Smonyan and Zsserman (2014)] network. 4.1 Object extracton

11 A Fuson Steganographc Algorthm Based on Faster R-CNN 11 When we extract the target area, we use the Faster R-CNN model on COCO2014 data set. When tranng, the learnng rate (base_lr) s set to 0.001, gamma s set to 0.1, momentum s set to 0.9 and weght decay s set to After teratons, the network model fle s obtaned. When testng, we use ths network model fle to get the object detecton area box of the mage. Fg. 7 s the mage obtaned after tranng the test mage through the Faster R-CNN model. (a) (b) (c) (d) (e) Fgure 7: The frst row s the fve mages from COCO2014 dataset used n testng process. The second row s the target area after the selecton by Faster R-CNN 4.2 Steganographc process After obtanng the coordnates of the object detecton regon of the mage, three dfferent steganographc algorthms are used to embed the secret nformaton n the regon to obtan the stego mage. The three spatal steganographc algorthms are respectvely S- UNIWARD, HUGO and WOW. Accordng to the dvson hash, dfferent regons correspond to dfferent steganography algorthms. Fg. 8 shows the test results after steganography. Cover Resdual map Stego Fgure 8: The frst row shows the test mages, the second row represents the resdual mages steganography by the proposed method, and the thrd row shows the stego mages

12 12 Copyrght 2018 Tech Scence Press CMC, vol.55, no.1, pp.1-16, 2018 (a) (b) (c) Fgure 9: (a), (b) and (c) are lne charts of the PSNR values, MSE values and SSIM values of the proposed method and the three tradtonal spatal algorthms, respectvely 4.3 Analyss the qualty of stego In ths secton, three dfferent mage qualty ndexes are used to evaluate the qualty of the stego mages, whch are MSE, PSRN, and SSIM. Through experments, t s found that the proposed method s superor to HUGO, S-UNIWARD and WOW steganography n three ndexes of PSNR, MSE and SSIM, ndcatng the least dstorton of the method. As shown n Tab. 1, the PSNR value of SMSA_ROI s hgher than that of HUGO, S-UNIWARD and WOW. The hghest value s db, whch s hgher than 9.24 db n WOW algorthms, ndcatng that the dstorton of MSA_ROI method s the smallest. The maxmum MSE value of the proposed method s , whch s lower than the other three methods, and the mnmum can be as low as The last evaluaton ndex SSIM, the four methods of SSIM value s not much dfference, but MSA_ROI s stll better than the other three algorthms. As shown n Fg. 9, the PSNR value of the proposed method s obvously hgher than the three tradtonal spatal algorthms. The MSE value of the proposed method s obvously lower than the other three methods, and the lowest s The proposed method s hgher than the three tradtonal spatal algorthms on the SSIM value, and the maxmum s up to

13 A Fuson Steganographc Algorthm Based on Faster R-CNN 13 Table 1: Comparson of the proposed algorthm and the sngle spatal algorthm n mage qualty PSNR MSE MSA_ROI_A HUGO_A S-UNIWARD _A WOW_A MSA_ROI_B HUGO_B S-UNIWARD _B WOW_B MSA_ROI_C HUGO_C S-UNIWARD _C WOW_C MSA_ROI_HUGO_D MSA_ROI_ S-UNIWARD _D MSA_ROI_WOW_D HUGO_D S-UNIWARD _D WOW_D MSA_ROI_E HUGO_E S-UNIWARD _E WOW_E Concluson and future work Ths paper has two man contrbutons. The frst one s to combne the object detecton method to select a complex texture regon, whch s sutable for hdng nformaton. The second one ntegrates the exstng multple spatal steganography algorthms nto a cover mage. Experments show that the proposed method s superor to the tradtonal spatal steganography algorthm. Future works to further move ths research ncludes the followng aspects. 1. Hde secret message n the foreground completely. 2. Swtch to dfferent object detecton methods. 3. Adjust the steganography algorthm adaptvely. Acknowledgement: Ths work s supported, n part, by the Natonal Natural Scence Foundaton of Chna under grant numbers U , U , , , , ; n part, by the Jangsu Basc Research Programs-Natural Scence Foundaton under grant numbers BK and BK ; n part, by the Prorty

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