The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Medical image analysis

A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.

A deep learning approach for the analysis of masses in mammograms with minimal user intervention.

http://www.sce.carleton.ca/faculty/adler/publications/2012/rahmati-2012-mammography-segmentation.pdf

Mammography segmentation with maximum likelihood active contours

We show the first clinical results using the level-set-based reconstruction algorithm for electrical impedance tomography (EIT) data. The level-set-based reconstruction method (LSRM) allows the reconstruction of non-smooth interfaces between image regions, which are typically smoothed by traditional voxel-based reconstruction methods (VBRMs). We develop a time difference formulation of the LSRM for 2D images. The proposed reconstruction method is applied to reconstruct clinical EIT data of a slow flow inflation pressure–volume manoeuvre in lung-healthy and adult lung-injury patients. Images from the LSRM and the VBRM are compared. The results show comparable reconstructed images, but with an improved ability to reconstruct sharp conductivity changes in the distribution of lung ventilation using the LSRM.

/pdf/level-set-based-reconstruction-algorithm-for-eit-lung-images-42jq6n3oto.pdf

Level-set-based reconstruction algorithm for EIT lung images: first clinical results.

This paper presents a computer-aided approach to enhancing suspicious lesions in digital mammograms. The developed algorithm improves on a well-known preprocessor filter named contrast-limited adaptive histogram equalization (CLAHE) to remove noise and intensity inhomogeneities. The proposed preprocessing filter, called fuzzy contrast-limited adaptive histogram equalization (FCLAHE), performs non-linear enhancement. The filter eliminates noise and intensity inhomogeneities in the background while retaining the natural gray level variations of mammographic images within suspicious lesions. We applied Catarious segmentation method (CSM) to compare the segmentation accuracy in two scenarios: when there is no preprocessing filter, and when the proposed preprocessing filter is applied to the original image. The proposed filter has been evaluated on 50 real mammographic images and the experimental results show an average increase of segmentation accuracy by 14.16% when the new filter is applied.

/pdf/a-new-preprocessing-filter-for-digital-mammograms-2f6x2rw2bh.pdf

A new preprocessing filter for digital mammograms

we present a region-based active contour approach to segmenting masses in digital mammograms. The algorithm developed in a Maximum Likelihood approach is based on the calculation of the statistics of the inner and the outer regions (defined by the contour). The Poisson distribution that has been deemed in the past adequate for modeling mammograms is applied as the probability density function. The Poisson distribution parameters are assumed unknown and are also estimated by the algorithm. We evaluate the performance of the algorithm on real mammographic images, given from the digital database for screening mammography (DDSM). The quantitative validation results demonstrate an average segmentation accuracy of 81% for 100 test images using the presented method.

Maximum Likelihood Active Contours Specialized for Mammography Segmentation

A major challenge for E-commerce and content-based businesses is the possibility of altering identity documents or other digital data. This paper shows a watermark-based approach to protect digital identity documents against a Print-Scan (PS) attack. We propose a secure ID card authentication system based on watermarking. For authentication purposes, a user/customer is asked to upload a scanned picture of a passport or ID card through the internet to fulfill a transaction online. To provide security in online ID card submission, we need to robustly encode personal information of ID card’s holder into the card itself, and then extract the hidden information correctly in a decoder after the PS operation. The PS operation imposes several distortions, such as geometric, rotation, and histogram distortion, on the watermark location, which may cause the loss of information in the watermark. An online secure authentication system needs to first eliminate the distortion of the PS operation before decoding the hidden data. This study proposes five preprocessing blocks to remove the distortions of the PS operation: filtering, localization, binarization, undoing rotation, and cropping. Experimental results with 100 ID cards showed that the proposed online ID card authentication system has an average accuracy of 99% in detecting hidden information inside ID cards after the PS process. The innovations of this study are the implementation of an online watermark-based authentication system which uses a scanned ID card picture without any added frames around the watermark location, unlike previous systems.

/pdf/watermarking-in-e-commerce-1r0adjxnpm.pdf

Peyman Rahmati

Papers

Mammography segmentation with maximum likelihood active contours

Level-set-based reconstruction algorithm for EIT lung images: first clinical results.

A new preprocessing filter for digital mammograms

Maximum Likelihood Active Contours Specialized for Mammography Segmentation

Watermarking in E-commerce