The use of tablet PCs is spreading rapidly, and accordingly users browsing and inputting personal information in public spaces can often be seen by third parties. Unlike conventional mobile phones and notebook PCs equipped with distinct input devices (e.g., keyboards), tablet PCs have touchscreen keyboards for data input. Such integration of display and input device increases the potential for harm when the display is captured by malicious attackers. This paper presents the description of reconstructing tablet PC displays via measurement of electromagnetic (EM) emanation. In conventional studies, such EM display capture has been achieved by using non-portable setups. Those studies also assumed that a large amount of time was available in advance of capture to obtain the electrical parameters of the target display. In contrast, this paper demonstrates that such EM display capture is feasible in real time by a setup that fits in an attaché case. The screen image reconstruction is achieved by performing a prior course profiling and a complemental signal processing instead of the conventional fine parameter tuning. Such complemental processing can eliminate the differences of leakage parameters among individuals and therefore correct the distortions of images. The attack distance, 2 m, makes this method a practical threat to general tablet PCs in public places. This paper discusses possible attack scenarios based on the setup described above. In addition, we describe a mechanism of EM emanation from tablet PCs and a countermeasure against such EM display capture.

Background: The combination of systematic evolution of ligands by exponential enrichment (SELEX) and deep sequencing is termed high-throughput (HT)-SELEX, which enables searching aptamer candidates from a massive amount of oligonucleotide sequences. A clustering method is an important procedure to identify sequence groups including aptamer candidates for evaluation with experimental analysis. In general, aptamer includes a specific target binding region, which is necessary for binding to the target molecules. The length of the target binding region varies depending on the target molecules and/or binding styles. Currently available clustering methods for HT-SELEX only estimate clusters based on the similarity of full-length sequences or limited length of motifs as target binding regions. Hence, a clustering method considering the target binding region with different lengths is required. Moreover, to handle such huge data and to save sequencing cost, a clustering method with fast calculation from a single round of HT-SELEX data, not multiple rounds, is also preferred.Results: We developed fast string-based clustering (FSBC) for HT-SELEX data. FSBC was designed to estimate clusters by searching various lengths of over-represented strings as target binding regions. FSBC was also designed for fast calculation with search space reduction from a single round, typically the final round, of HT-SELEX data considering imbalanced nucleobases of the aptamer selection process. The calculation time and clustering accuracy of FSBC were compared with those of four conventional clustering methods, FASTAptamer, AptaCluster, APTANI, and AptaTRACE, using HT-SELEX data (>15 million oligonucleotide sequences). FSBC, AptaCluster, and AptaTRACE could complete the clustering for all sequence data, and FSBC and AptaTRACE performed higher clustering accuracy. FSBC showed the highest clustering accuracy and had the second fastest calculation speed among all methods compared.Conclusion: FSBC is applicable to a large HT-SELEX dataset, which can facilitate the accurate identification of groups including aptamer candidates.Availability of data and materials: FSBC is available athttp://www.aoki.ecei.tohoku.ac.jp/fsbc/.

This paper presents an implementation of iris recognition algorithm using phase-based image matching–an image matching technique using phase components in 2D discrete Fourier transforms (DFTs) of given images. Our experimental observation clearly shows that the use of phase components of iris images makes possible to achieve highly accurate iris recognition even for low-quality iris images. In this paper, we consider the problem of designing a compact phase-based iris recognition algorithm especially suitable for hardware implementation. We also present prototype implementation of an iris recognition system based on the proposed algorithm. The prototype system fully utilizes state-of-the-art DSP (digital signal processor) technology to achieve real-time iris recognition capability within a compact hardware module.

This paper proposes a new debiasing method and its application to fuzzy extractors (FEs) for stable and efficient extraction of uniform random binary responses from biased physically unclonable functions (PUFs). The proposed method handles PUF responses as ternary responses assuming that an unstable response bit is the third value, and stably extracts uniform random binary responses from them. In this paper, we evaluate the stability and effectiveness of the proposed method via two experiments with simulated and actual responses of latch PUFs. We demonstrate that the proposed method can obtain longer debiased random binary responses than the conventional method. In addition, we apply the debiasing method to the construction of FEs; in an experimental evaluation, we show the advantages of the proposed FE in terms of needed PUF size and authentication success rate. The results show the advantage of the proposed method to other conventional methods. In particular, the needed PUF sizes in the proposed FE were 34%-38% shorter than those in the conventional binary method under the condition that the authentication success rates are sufficient.

In this paper, we propose an image completion algorithm which takes advantage of the countless number of images available on Internet photo sharing sites to replace occlusions in an input image. The algorithm 1) automatically selects the most suitable images from a database of downloaded images and 2) seamlessly completes the input image using the selected images with minimal user intervention. Experimental results on input images captured at various locations and scene conditions demonstrate the effectiveness of the proposed technique in seamlessly reconstructing user-defined occlusions.

In biometrics, the secure transfer and storage of biometric samples are important for protecting the privacy and security of the data subject. One of the methods for authentication while protecting biometric samples is cancelable biometrics, which performs transformation of features and uses the transformed features for authentication. Among the methods of cancelable biometrics, steganography-based approaches have been proposed, in which secret information is embedded in another to hide its existence. In this paper, we propose cancelable biometrics based on deep steganography for face recognition. We embed a face image or its face features into a cover image to generate a stego image with the same appearance as the cover image. By using a dedicated face feature extractor, we can perform face recognition without restoring the embedded face image or face features from the stego image. We demonstrate the effectiveness of the proposed method compared to conventional steganography-based methods through performance and security evaluation experiments using public face image datasets. In addition, we present one of the potential applications of the proposed method to improve the security of face recognition by using a QR code with a one-time password for the cover image.