This paper presents a dynamic regular groups steganalysis (DRS) algorithm to detect LSB steganography. This algorithm dynamically selects an appropriate mask for each image to reduce the initial bias, and estimates' the LSB embedding message ratio by constructing equations with the statistics of regular groups in image. Experimental results show that this algorithm is more accurate and has a lower missing rate and false1 alarm rate than the conventional RS method and some other powerful steganalysis approaches present recently.
{"title":"Detecting LSB steganography based on dynamic masks","authors":"Xiangyang Luo, B. Liu, Fenlin Liu","doi":"10.1109/ISDA.2005.37","DOIUrl":"https://doi.org/10.1109/ISDA.2005.37","url":null,"abstract":"This paper presents a dynamic regular groups steganalysis (DRS) algorithm to detect LSB steganography. This algorithm dynamically selects an appropriate mask for each image to reduce the initial bias, and estimates' the LSB embedding message ratio by constructing equations with the statistics of regular groups in image. Experimental results show that this algorithm is more accurate and has a lower missing rate and false1 alarm rate than the conventional RS method and some other powerful steganalysis approaches present recently.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127182995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When evaluating the performance of a stochastic optimizer it is sometimes desirable to express performance in terms of the quality attained in a certain fraction of sample runs. For example, the sample median quality is the best estimator of what one would expect to achieve in 50% of runs, and similarly for other quantiles. In multiobjective optimization, the notion still applies but the outcome of a run is measured not as a scalar (i.e. the cost of the best solution), but as an attainment surface in k-dimensional space (where k is the number of objectives). In this paper we report an algorithm that can be conveniently used to plot summary attainment surfaces in any number of dimensions (though it is particularly suited for three). A summary attainment surface is defined as the union of all tightest goals that have been attained (independently) in precisely s of the runs of a sample of n runs, for any s/spl isin/1..n, and for any k. We also discuss the computational complexity of the algorithm and give some examples of its use. C code for the algorithm is available from the author.
{"title":"A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers","authors":"Joshua D. Knowles","doi":"10.1109/ISDA.2005.15","DOIUrl":"https://doi.org/10.1109/ISDA.2005.15","url":null,"abstract":"When evaluating the performance of a stochastic optimizer it is sometimes desirable to express performance in terms of the quality attained in a certain fraction of sample runs. For example, the sample median quality is the best estimator of what one would expect to achieve in 50% of runs, and similarly for other quantiles. In multiobjective optimization, the notion still applies but the outcome of a run is measured not as a scalar (i.e. the cost of the best solution), but as an attainment surface in k-dimensional space (where k is the number of objectives). In this paper we report an algorithm that can be conveniently used to plot summary attainment surfaces in any number of dimensions (though it is particularly suited for three). A summary attainment surface is defined as the union of all tightest goals that have been attained (independently) in precisely s of the runs of a sample of n runs, for any s/spl isin/1..n, and for any k. We also discuss the computational complexity of the algorithm and give some examples of its use. C code for the algorithm is available from the author.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114953119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Szymon Borak, Matthias R. Fengler, Wolfgang Hiirdle
Implied volatility is one of the key issues in modern quantitative finance, since plain vanilla option prices contain vital information for pricing and hedging of exotic and illiquid options. European plain vanilla options are nowadays widely traded, which results in a great amount of high-dimensional data especially on an intra day level. The data reveal a degenerated string structure. Dynamic semiparametric factor models (DSFM) are tailored to handle complex, degenerated data and yield low dimensional representations of the implied volatility surface (IVS). We discuss estimation issues of the model and apply it to DAX option prices.
{"title":"DSFM fitting of implied volatility surfaces","authors":"Szymon Borak, Matthias R. Fengler, Wolfgang Hiirdle","doi":"10.2139/ssrn.2894415","DOIUrl":"https://doi.org/10.2139/ssrn.2894415","url":null,"abstract":"Implied volatility is one of the key issues in modern quantitative finance, since plain vanilla option prices contain vital information for pricing and hedging of exotic and illiquid options. European plain vanilla options are nowadays widely traded, which results in a great amount of high-dimensional data especially on an intra day level. The data reveal a degenerated string structure. Dynamic semiparametric factor models (DSFM) are tailored to handle complex, degenerated data and yield low dimensional representations of the implied volatility surface (IVS). We discuss estimation issues of the model and apply it to DAX option prices.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116145751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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