Computational Forensics: Second International Workshop, IWCF by Sargur N. Srihari, Katrin Franke

By Sargur N. Srihari, Katrin Franke

This ebook constitutes the refereed complaints of the second one foreign Workshop, IWCF 2008, held in Washington, DC, united states, August 2008.

The 19 revised complete papers offered have been conscientiously reviewed and chosen from 39 submissions. The papers are prepared in topical sections on tendencies and demanding situations; scanner, printer, and prints; human identity; shoeprints; linguistics;decision making and seek; speech research; signatures and handwriting.

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Extra resources for Computational Forensics: Second International Workshop, IWCF 2008, Washington, DC, USA, August 7-8, 2008. Proceedings

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Accuracy rate for all tested features, classified by a SVM using a rbf kernel and optimized parameters for feature extraction and classification Evaluation of Graylevel-Features for Printing Technique Classification with SVM (Top 3 Features) 100 100 80 80 Accuracy Rate [%] Accuracy Rate [%] Classification with SVM (All Features) 43 60 40 20 60 40 20 0 0 0 100 200 300 Resolution [dpi] 400 500 0 100 200 300 400 500 Resolution [dpi] Fig. 5. Box plot of a SVM classification with a combination of all (l) and the 3 most discriminating features as identified by pca (r) Training results: Overall, the classification results in Fig.

Determining image origin and integrity using sensor noise. IEEE Transactions on Information Forensics and Security 3(1), 74–90 (2008) 7. : Scanner identification using sensor pattern noise. In: Proceedings of the SPIE International Conference on Security, Steganography, and Watermarking of Multimedia Contents IX. SPIE, vol. 6505, p. 65051K (2007) 8. : A novel local polynomial estimator based on directional multiscale optimizations. In: Proceedings of the 6th IMA Int. Conf. Math. in Signal Processing, vol.

Box plot of a SVM classification with a combination of all (l) and the 3 most discriminating features as identified by pca (r) Training results: Overall, the classification results in Fig. 4 are slightly lower than for decision trees considering single features. Also a more constant development of the curves for resolutions > 200dpi can be observed (Fig. 2). Furthermore, a higher classification accuracy at 400dpi using all features is achieved. Testing results: As for the single feature evaluation, the box plots in Fig.

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