Turning Cost Based Steganography Into Model Based-Books Pdf

Turning Cost Based Steganography into Model Based
15 Jul 2020 | 4 views | 0 downloads | 9 Pages | 725.98 KB

Share Pdf : Turning Cost Based Steganography Into Model Based

Download and Preview : Turning Cost Based Steganography Into Model Based

Report CopyRight/DMCA Form For : Turning Cost Based Steganography Into Model Based



Transcription

where Ii is the steganographic Fisher information 8 17 at cover payload dependent costs also see Section 5 Fig 2 in 11 In this. element i In particular the optimal change rates satisfy for each i section we explore this idea in reverse. The formula for costs is usually derived heuristically through. i Ii H 3 i 4 feedback provided by empirical steganalysis For example when. designing HILL 23 the authors experimented with various sizes. where H 3 x is the derivative of H 3 x subject to the same payload of the two low pass filters The authors of UNIWARD 14 16. constraint In practice this is usually done by solving 4 and 2 explored different wavelet bases and their supports as well as a. numerically with a binary search over 11 24 25 range of values for the stabilizing constant 6 And this is usually. MiPOD 24 is an example of a steganographic scheme that done for a fixed relative payload selected so that the detectability. minimizes the power of the most powerful detector an adversary is not too small or too large to better see the impact of various. can build when modeling the noise residuals in a digital image as design choices In the spatial domain the payload size of 0 4 bpp. independent realizations of zero mean Gaussian random variables bits per pixel is a popular choice also because it has been used. with variances i2 estimated for each cover element i In this case in the steganalysis competition BOSS 1 Thus it is reasonable to. the steganographic Fisher information is Ii 1 i4 in the fine assume that this empirical process leads to an embedding scheme. that is near optimal for the chosen payload and the dataset given the. quantization limit i2 1, current status of steganalysis It has already been shown in 26 that. Feature Correction Method FCM 20 and approaches based. steganography tends to be over optimized for a given source of. on embedding while minimizing distance in some feature space. images This is confirmed by the above observation that both HILL. such as ASO 22 and Adv Emb 29 are not truly model based. and MiPOD achieve a similar level of empirical detectability and. because there is no underlying statistical model there but are again. the fact that no substantial improvement in additive steganography. distortion based with the measure of distortion computed as some. has been reported in the past six years of rather intense research. distance in a selected feature space, Thus we make an assumption that given some embedding. In this paper we research the possibility to interpret cost based. scheme with costs i there exists a relative payload D bpp which. embedding schemes as model based schemes similar to MiPOD We. we call the design payload for which the embedding change rates. start with the assumption that for some relative payload m N. the embedding change rates i computed from the costs as in 1 i D derived from the costs are near optimal for the current status. are the optimal change rates for some unknown cover model of steganalysis Then we derive the corresponding Fisher informa. derive the corresponding Fisher information and then for all other tion for each pixel Ii D so that the deflection 2 12 i 1 i Ii. payloads we embed by minimizing the deflection 3 We expect achieves its minimum value when i i D under the same pay. the improvement in security to be especially noticeable for the case load constraint Using the method of Lagrange multipliers it can. of a knowledgeable adversary who knows the embedding change be easily shown that this happens exactly when. rates i i e when steganalyzing with SCA rich models or SCA. versions of CNN detectors D i, In the next section we explain the main idea behind converting Ii 5. a cost based scheme to a model based one Section 3 contains the i. results of experiments with HILL and WOW The improvement Having determined the Fisher information for each pixel we. in security is shown on two datasets with detectors built as rich can now embed other payload sizes D by minimizing the. models as well as deep CNNs JPEG domain schemes J UNIWARD deflection. and UED JC are studied experimentally in Section 4 for two qual. ity factors 75 and 95 The reported gains are especially large for 1 2 D. UED and for the smaller quality factor Interpreting HILL s costs as 2 i 1 i i. reciprocals of local standard deviation estimates in Section 5 we. study a version of MiPOD with this different variance estimator subject to i 1 H 3 i N A graphical representation of above. The paper is summarized in Section 6 protocol is shown in Figure 1. Note that this approach does not inform us about the model that. 2 COSTS TO MODEL is responsible for the steganographic Fisher information We merely. determine Ii which could correspond to many different models. A brief inspection of the current literature on steganalysis in spatial. domain e g 2 reveals that cost based steganographic systems. that do not use side information at the sender such as HILL 23 3 SPATIAL DOMAIN. exhibit approximately the same level of empirical security as the In this section we focus on spatial domain steganographic algo. model based MiPOD 24 Fundamentally however they are very rithms HILL 23 and WOW 15 Since both have been designed on. different with HILL minimizing an objective function that is linear the standard dataset BOSSbase 1 01 1 containing 10 000 512 512. in change rates while MiPOD minimizes deflection which is qua grayscale images we search for the best design payload D on the. dratic in change rates Since practical embedding with the model same dataset unless mentioned otherwise The FLD ensemble 21. based MiPOD requires converting the optimal change rates deter with the spatial rich model SRM 10 and maxSRM 7 was trained. mined by 4 to costs by inverting 1 and applying syndrome trellis on 5 000 randomly selected images and tested on the remaining. codes one can interpret MiPOD as an embedding scheme with 5 000. HILL SRM HILL maxSRMd2, D 0 05 0 1 0 2 0 3 0 4 0 5 D 0 05 0 1 0 2 0 3 0 4 0 5. 0 05 0 4739 0 4416 0 3636 0 2951 0 2454 0 2017 0 05 0 4307 0 3732 0 2916 0 2373 0 1901 0 1507. 0 1 0 4712 0 4364 0 3735 0 3065 0 2503 0 1994 0 1 0 4452 0 3909 0 3067 0 2446 0 2009 0 1604. 0 2 0 4643 0 4336 0 3669 0 3106 0 2525 0 2097 0 2 0 4457 0 4024 0 3189 0 2622 0 2126 0 1691. 0 3 0 4587 0 4303 0 3639 0 3056 0 2537 0 2067 0 3 0 4484 0 4056 0 3282 0 2711 0 2249 0 1821. 0 4 0 4544 0 4206 0 3666 0 3067 0 2525 0 2115 0 4 0 4502 0 4025 0 3327 0 2706 0 2291 0 1903. 0 5 0 4548 0 4127 0 3481 0 3005 0 2475 0 2077 0 5 0 4440 0 4031 0 3353 0 2769 0 2301 0 1939. Table 1 Detection error PE of model based HILL for different design payloads D and embedded payloads Left SRM Right. maxSRMd2 ensemble classifier BOSSbase Regular HILL corresponds to the diagonal D. i Search for D 0 5, Cover Ii D 0 4, Figure 1 Embedding relative message bpp bpnzac with 0 2.
design payload D for arbitrary cost based steganographic. scheme Notice that the costs i are used only to compute. the Fisher Information for each pixel Ii D, 0 05 0 1 0 2 0 3 0 4 0. Regular SRNet 0 3893 0 3192 0 2325 0 1779 0 1465, HILL SCA SRNet 0 3992 0 3164 0 2167 0 1717 0 1360 0 0 1 0 2 0 3 0 4. MB HILL SRNet 0 4188 0 3468 0 2449 0 1811 0 1444 Relative Payload. D 0 5 SCA SRNet 0 4751 0 3591 0 2387 0 1777 0 1393. Table 2 Detection error PE of SRNet and SCA SRNET for Figure 2 Detection error PE of the best detector SRNet or. HILL and model based HILL D 0 5 bpp in downsampled SCA SRNet for HILL and model based HILL D 0 5 bpp. BOSSbase BOWS2 in downsampled BOSSbase BOWS2, whose images were downsampled to 256 256 pixels using Matlab s. imresize with default parameters As in 2 31 this 20 000 image. 3 1 Model based HILL dataset was split into 14 000 10 000 BOWS2 and 4 000 randomly. Table 1 shows the results for HILL in terms of PE the total classifi chosen from BOSSbase for training 1 000 BOSSbase images for. cation error under equal priors for the cover and stego classes The validation and 5 000 for testing. boldface font highlights the most secure algorithm version which is Technically the design payload should be searched for anew for. to be compared with the diagonal D corresponding to regu this dataset and detector Due to the much more computationally. lar HILL Note that the results are vastly different depending on the demanding training of the SRNet however we only compare model. steganalysis features For SRM which is an ignorant adversary one based HILL for D 0 5 and regular HILL Table 2 Comparing the. who does not use the knowledge of the selection channel there best detector SRNet vs SCA SRNet2 for each embedding algorithm. is no clear design payload that would always give the best results in Figure 2 we observe an empirical gain in security ranging from. Also the impact on security is quite small In contrast detection almost 3 for the smallest payloads to almost no gain for 0 4. with a knowledgeable adversary maxSRMd2 indicates that the. best overall design payload is D 0 5 bpp for the two smallest 3 2 Model based WOW. tested payloads the differences between D 0 3 0 4 and 0 5 are Searching for the best design payload on BOSSbase with maxSRMd2. small The largest boost in empirical security is 1 7 for payload and the ensemble classifier it also appears to be close to D. 0 2 0 5 bpp Since WOW is known to be overly content adaptive in. We repeated the same experiment with the CNN SRNet and its the sense that its security decreases significantly with selection. SCA version 2 Because large CNNs such as the SRNet cannot be channel aware attacks the impact of making it model based is. trained on 512 512 images on GPUs with 12 GB memory with a. reasonable batch size we used the union of BOSSbase and BOWS2 2 In some cases SCA SRNet performs worse than SRNet. 0 5 4 JPEG DOMAIN, WOW In the JPEG domain we investigated the embedding algorithms. MB WOW J UNIWARD and UED JC 13 For the database of larger 512 512. images we steganalyzed with selection channel aware Gabor Phase. Aware Residuals SCA GFR 4 28 while as above the SRNet and. 0 3 SCA SRNet were used on the database of downsampled images. The split of the datasets was the same as for the experiments in the. 0 2 spatial domain, 4 1 J UNIWARD, 0 1 For J UNIWARD the results are graphically displayed in Figure 5.
showing the detection error of J UNIWARD and its model based. 0 version with D 0 6 bpnzac The gain in security is generally. much larger than what was observed in the spatial domain Also. 0 0 1 0 2 0 3 0 4 it is larger for quality factor 75 than for 95 As before the gain. Relative Payload increases with decreasing payload In particular for quality 75 the. gain was up to 3 5 with SCA GFR and 7 3 with SCA SRNet While. Figure 3 Detection error PE of maxSRMd2 for WOW and we observed almost no gain for quality 95 with SCA GFR the better. model based WOW D 0 5 bpp in BOSSbase detector SCA SRNet showed more than 8 of improvement for. the smallest payload, The embedding algorithm UED JC benefits from our approach by. far the most out of all tested stego methods in any domain Figure 6. 0 4 shows the detection error achieved on BOSSbase with SCA GFR. and on the downsampled images with SCA SRNet for two quality. factors The gain is again larger on downsampled images when. detecting with SCA SRNet and is over 12 for the smallest payload. On BOSSbase with SCA GFR the gain on the smallest payload. 0 2 is about 10 In both datasets the gain diminishes to zero as. approaches D, The actual values of the detection error from the graphs for J. 0 1 UNIWARD and UED JC appear in Table 4 at the end of this paper. 5 INTERPRETING HILL S COSTS, 0 0 1 0 2 0 3 0 4 The main contribution of this paper is the realization that there. Relative Payload is a cover model behind cost based schemes and a method for. estimating the model its Fisher information In this section we. Figure 4 Detection error PE of the best detector SRNet or take a closer look at the embedding algorithm HILL and interpret. SCA SRNet for WOW and model based WOW D 0 7 its costs as reciprocal estimates of the local standard deviation. bpp in downsampled images BOSSbase BOWS2 Equipped with this insight we implement a model based version of. HILL with a Gaussian model of pixel residual which is essentially. a version of MiPOD with a different variance estimator. HILL High pass Low pass Low pass computes costs heuristi. larger than for HILL The detection error P E shown in Figure 3 cally using a series of filtering operations First the 3 3 high pass. is about 4 larger for the two smallest payloads for model based KB filter 18 F KB is applied to the cover image X producing the KB. WOW than for the original cost based algorithm residual R X F KB Next the absolute value of the KB residual is. On the dataset of downsampled images based on our investiga smoothed with a 3 3 averaging filter A3 3 R A3 3 Finally the. tion with maxSRMd2 the best design payload is larger D 0 7 reciprocal of this signal is smoothed by applying a 15 15 averaging. bpp In Figure 4 we contrast the detection error of SRNet on model filter A15 15. based WOW and WOW ranges from 3 4 for the smallest payload. of 0 05 bpp to 0 7 for 0 2 bpp The empirical security of both al 1. gorithms appears similar for the two largest payloads The actual R A3 3. values of the detection error appear in Table 4 at the end of this Ignoring the second low pass filtering in Equation 7 for simplic. paper ity the costs can be seen as reciprocal expectation of the absolute. Turning Cost Based Steganography into Model Based Jan Butora Yassine Yousfi and Jessica Fridrich Binghamton University Department of Electrical and Computer Engineering Binghamton NY 13850 jbutora1 yyousfi1 fridrich binghamton edu ABSTRACT Most modern steganographic schemes embed secrets by minimiz ing the total expected cost of

Related Books

Social Media Insights How Digital Marketers EngageonTwier

Social Media Insights How Digital Marketers EngageonTwier

2 2 2 4 5 6 6 9 25 27 34 52 100 Pandora Untappd Quora Yelp Path Triberr Facebook Tumblr Pinterest GetGlue Spotify LinkedIn Vine foursquare Instagram Twitter In addition to Twitter digital marketing pros are active on a number of other social networks many of which cross post content to Twitter The most popular networks with digital marketers are dominated by media centric

Towards a language independent Twitter bot detector

Towards a language independent Twitter bot detector

Towards a language independent Twitter bot detector Jonas Lundberg1 Jonas Nordqvist2 and Mikko Laitinen3 1 D ep ar tm nof C u Sc i L sU v y V xj w d 2 D ep ar tm nof M h ic s L u U v y V xj Sw d 3 Sch o lfH um anit e s U v ry E F dJ Abstract This article describes our work in developing an application that recognizes automatically generated tweets

RECIPRO SAW MODEL JR3000V Makita USA

RECIPRO SAW MODEL JR3000V Makita USA

FIG TYPE 1 Plane Bearing 12 7 The above parts are interchangeable as as set 5 18 8 6 DESCRIPTION TYPE 2 Part 214157 3 Discontinued Part 214166 2

an u m pustakalay

an u m pustakalay

wn Imonq m qe m re a ql u joK m t o lov u j p ze no m no T y u ke a m hen t n o b dl o xoQ jy r e amoirk j y ty re a lt iPs m g ull I m rIno v s ul krI lov xo a v s I Aqlo xoQn I B Im k y p tn I a m t o An u 20no b dlo 40 kIl o v jn G q zv m a vo

Title Impact Assessment IA Amazon S3

Title Impact Assessment IA Amazon S3

Impact Assessment IA Date 14 December 2016 Stage Final Report Source of intervention Domestic Type of measure Primary legislation Contact for enquiries Spencer Clarke 02033343152 spencer clarke lawcommission gsi gov uk Summary Intervention and Options RPC Opinion RPC Opinion Status Cost of Preferred or more likely Option Total Net Present Value Business Net Present Value Net

Impact Assessment IA

Impact Assessment IA

Title Trailer registration IA No DfT00396 Lead department or agency DfT Other departments or agencies DVLA Impact Assessment IA Date 7 2 2018 Stage Development Options Source of intervention Domestic Type of measure Primary Legislation Contact for enquiries Paul O Sullivan HaulageTrailersBill dft gsi gov uk Summary Intervention and Options RPC Opinion Green Cost of Preferred or

Title Reforming the packaging producer Impact Assessment IA

Title Reforming the packaging producer Impact Assessment IA

Title Reforming the packaging producer responsibility system in the United Kingdom IA No RPC Reference No Lead department or agency Department for Environment Food and Rural Affairs Defra Other departments or agencies Impact Assessment IA Date 14 02 2019 Stage Consultation Source of intervention Domestic Type of measure Primary legislation Contact for enquiries Ladislav

Title Extending fixed recoverable costs FRC Impact

Title Extending fixed recoverable costs FRC Impact

Title Extending fixed recoverable costs FRC IA No RPC reference Number Lead department or agency Ministry of Justice Other departments or agencies N A general queries justice gsi gov uk Impact Assessment IA Date 08 02 2019 Stage Consultation Source of intervention Domestic Type of measure Contact for enquiries Summary Intervention and Options RPC Opinion N A Cost of Preferred

Impact Assessment IA Citizen Space

Impact Assessment IA Citizen Space

Title Consultation Stage IA The Renewable Heat Incentive A reformed and refocused scheme IA No DECC0211 Lead department or agency Department of Energy and Climate Change Other departments or agencies N A Impact Assessment IA Date 03 03 2016 Stage Development Options Source of intervention Domestic Type of measure Secondary legislation Contact for enquiries RHI decc gsi gov uk

Title Impact Assessment IA IA No LAWCOM0061

Title Impact Assessment IA IA No LAWCOM0061

Impact Assessment IA Date Stage Development Options BUSINESS ASSESSMENT Option 1 Direct impact on business Equivalent Annual m Score for Business Impact Target qualifying provisions only m Costs Benefits Net 1 Not available not zero 2 Annual savings based on best estimates 3 Summary Analysis amp Evidence Policy Option 2 Description Limited reform Secondary