By Satoshi Hada, Kouichi Sakurai (auth.), Atsuko Miyaji, Hiroaki Kikuchi, Kai Rannenberg (eds.)
The overseas Workshop on safety (IWSEC 2007) was once the second one within the annualseriesthat startedin 2006.IWSEC 2007washeld on the New Public corridor in Nara, Japan, in the course of October 29–31, 2007. This yr there have been 112 paper submissions, and from those 30 papers have been accredited. authorized papers got here from 27 di?erent nations, with the biggest percentage coming from Japan (12). Estonia, China, Korea, Spain, Taiwan and america contributed 2 papers each one and Canada, Germany, Greece, Poland, Turkeyand Vietnam contributed 1 paper every one. we'd liketo thank all the authors who submitted papers to IWSEC 2007. The contributed papers have been supplemented through one invited speak from the - inent researcher Prof. Doug Tygar (UC Berkeley) in info protection. We have been lucky to have an lively workforce of specialists who shaped this system Committee. Their names should be discovered overleaf, and we're in actual fact thankful for all their nice e?orts. This crew was once supported through a good greater variety of people who reviewedpapers of their particularareasof services. an inventory of those names can be supplied; we are hoping it really is complete.
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Additional resources for Advances in Information and Computer Security: Second International Workshop on Security, IWSEC 2007, Nara, Japan, October 29-31, 2007. Proceedings
In a ﬁngerprinting scheme, each watermarked copy is slightly diﬀerent, hence, malicious users will collect t copies D1 , . . , Dt with respective watermark X 1 , . . , X t in order to remove/alter the watermark. A simple, yet eﬀective way is to average them because when t copies are averaged, D∗ = (D1 + . . + Dt )/t, the similarity value calculated by Eq. (1) results in shrinking by a factor of t, √ which will be roughly n/t . Even in this case, we can detect the embedded watermark and identify the colluders by using an appropriately designed threshold.
4) approximate to that of non-watermarked components, which is an ideal estimation. In 2 , which accuracy will be a basic scheme, all components are used to calculate σm degraded by the watermarked components. In order to exclude such components eﬃciently, Eq. (4) is applied in our optimized scheme. The results are shown in Table 2. It is certiﬁed that the optimized scheme can obtain proper variances, hence thresholds can be designed based on the statistical property. 2 0 -40 -20 0 0 20 40 60 80 100 120 the size DCT coefficient (a) Detection sequence dˆ0.
Fig. 26 dB. (a) Original image (b) Watermarked image α0 = α1 = 500 Fig. 5. 2 Evaluation of Statistical Property In our scheme, we set a threshold based on the statistical property of the distribution of each detection sequence. In order to examine the property, we ﬁrst show the histogram of detection sequence dˆ0 and dˆ1 in Fig. 6 (a) and (b), respectively. In the simulation, watermarked images are averaged by 10 colluders with random groups and performed JPEG compression with quality 35 %. We set the watermark strength α0 = α1 = 500, and evaluate the performance using randomly selected 103 patterns of user ID.
Advances in Information and Computer Security: Second International Workshop on Security, IWSEC 2007, Nara, Japan, October 29-31, 2007. Proceedings by Satoshi Hada, Kouichi Sakurai (auth.), Atsuko Miyaji, Hiroaki Kikuchi, Kai Rannenberg (eds.)