Advances in fingerprint authentication technology have led to it being used in a growing range of personal devices such as PCs and smartphones. However, they have also made it possible to capture fingerprints remotely with a digital camera, putting the target person at risk of illegal log-ins and identity theft. This article shows how fingerprint captured in this manner can be authenticated and how people can protect their fingerprints against surreptitious photography. First we show that photographed fingerprints have enough information to spoof fingerprint authentication systems by demonstrating with “fake fingers” made from such photographs. Then we present a method that defeats the use of surreptitious photography without preventing the use of legitimate fingerprint authentication devices. Finally, we demonstrate that an implementation of the proposed method called “BiometricJammer,” a wearable device put on a fingertip, can effectively prevent the illegal acquisition of fingerprints by surreptitious photography while still enabling contact-based fingerprint sensors to respond normally.

The study of universal detector for fingerprinting code is strongly dependent on the design of scoring function. The optimal detector is known as MAP detector that calculates an optimal correlation score for a given single user's codeword. However, the knowledge about the number of colluders and their collusion strategy are inevitable. In this paper, we propose a new scoring function that equalizes the bias between symbols of codeword, which is called bias equalizer. We further investigate an efficient scoring function based on the bias equalizer under the relaxed marking assumption such that white Gaussian noise is added to a pirated codeword. The performance is compared with the MAP detector as well as some state-of-the-art scoring functions.

We present the concept of key-rotatable and security-updatable homomorphic encryption (KR-SU-HE) scheme, which is defined as a class of public-key homomorphic encryption in which the keys and the security of any ciphertext can be rotated and updated while still keeping the underlying plaintext intact and unrevealed. After formalising the syntax and security notions for KR-SU-HE schemes, we build a concrete scheme based on the Learning With Errors assumption. We then perform several careful implementations and optimizations to show that our proposed scheme is efficiently practical.

This paper proposes a novel secure biometric authentication scheme. The scheme deals with fingerprint minutiae as the biometric feature and the matching is checked by a widely used technique. To discuss security, we formalize the model of secure biometric authentication scheme by abstracting the related and proposed schemes. The schemes which satisfy all the proposed security requirements are guaranteed to prevent leakage of biometric information and impersonation. In particular, the definition captures well-known and practical attacks including replay attacks and hill-climbing attacks. We prove that the proposed scheme achieves all the requirements if the additive homomorphic encryption scheme used in the scheme satisfies some additional properties. As far as we know, the proposed scheme is the first one that satisfies all the requirements. Also, we show that modified Elgamal cryptosystem satisfies all the properties under the decisional Diffie-Hellman assumption.

With an annual growth of billions of sensor-based devices, it is an urgent need to do stream mining for the massive data streams produced by these devices. Cloud computing is a competitive choice for this, with powerful computational capabilities. However, it sacrifices real-time feature and energy efficiency. Application-specific integrated circuit (ASIC) is with high performance and efficiency, which is not cost-effective for diverse applications. The general-purpose microcontroller is of low performance. Therefore, it is a challenge to do stream mining on these low-cost devices with scalability and efficiency. In this paper, we introduce an FPGA-based scalable and parameterized architecture for stream mining.Particularly, Dynamic Time Warping (DTW) based k-Nearest Neighbor (kNN) is adopted in the architecture. Two processing element (PE) rings for DTW and kNN are designed to achieve parameterization and scalability with high performance. We implement the proposed architecture on an FPGA and perform a comprehensive performance evaluation. The experimental results indicate thatcompared to the multi-core CPU-based implementation, our approach demonstrates over one order of magnitude on speedup and three orders of magnitude on energy-efficiency.

In this paper, we propose a received signal interpolation method for enhancing the performance of multiple signal classification (MUSIC) algorithm. In general, the performance of the conventional MUSIC algorithm is very sensitive to signal-to-noise ratio (SNR) of the received signal. When array elements receive the signals with nonuniform SNR values, the resolution performance is degraded compared to elements receiving the signals with uniform SNR values. Hence, we propose a signal calibration technique for improving the resolution of the algorithm. First, based on original signals, rough direction of arrival (DOA) estimation is conducted. In this stage, using frequency-domain received signals, SNR values of each antenna element in the array are estimated. Then, a deteriorated element that has a relatively lower SNR value than those of the other elements is selected by our proposed scheme. Next, the received signal of the selected element is spatially interpolated based on the signals received from the neighboring elements and the DOA information extracted from the rough estimation. Finally, fine DOA estimation is performed again with the calibrated signal. Simulation results show that the angular resolution of the proposed method is better than that of the conventional MUSIC algorithm. Also, we apply the proposed scheme to actual data measured in the testing ground, and it gives us more enhanced DOA estimation result.

One of the problems associated with voice conversion from a nonparallel corpus is how to find the best match or alignment between the source and the target vector sequences without linguistic information. In a previous study, alignment was achieved by minimizing the distance between the source vector and the transformed vector. This method, however, yielded a sequence of feature vectors that were not well matched with the underlying speaker model. In this letter, the vectors were selected from the candidates by maximizing the overall likelihood of the selected vectors with respect to the target model in the HMM context. Both objective and subjective evaluations were carried out using the CMU ARCTIC database to verify the effectiveness of the proposed method.

Multivariate Public Key Cryptography (MPKC) is one of the main candidates for secure communication in a post-quantum era. Recently, Yasuda and Sakurai proposed at ICICS 2015 a new multivariate encryption scheme called SRP, which offers efficient decryption, a small blow up factor between plaintext and ciphertext and resists all known attacks against multivariate schemes. However, similar to other MPKC schemes, the key sizes of SRP are quite large. In this paper we propose a technique to reduce the key size of the SRP scheme, which enables us to reduce the size of the public key by up to 54%. Furthermore, we can use the additional structure in the public key polynomials to speed up the encryption process of the scheme by up to 50%. We show by experiments that our modifications do not weaken the security of the scheme.

Radio signals show characteristics of minute differences, which result from various idiosyncratic hardware properties between different radio emitters. A robust detector based on exponentially weighted distances is proposed to detect the exact reference instants of the burst communication signals. Based on the exact detection of the reference instant, in which the radio emitter finishes the power-up ramp and enters the first symbol of its preamble, the features of the radio fingerprint can be extracted from the transient signal section and the steady-state signal section for radiometric identification. Experiments on real data sets demonstrate that the proposed method not only has a higher accuracy that outperforms correlation-based detection, but also a better robustness against noise. The comparison results of different detectors for radiometric identification indicate that the proposed detector can improve the classification accuracy of radiometric identification.

Hotspots occur frequently in 3D multi-core processors (3D-MCPs), and they may adversely impact both the reliability and lifetime of a system. We present a new thermally constrained task scheduler based on a thermal-pattern-aware voltage assignment (TPAVA) to reduce hotspots in and optimize the performance of 3D-MCPs. By analyzing temperature profiles of different voltage assignments, TPAVA pre-emptively assigns different initial operating-voltage levels to cores for reducing temperature increase in 3D-MCPs. The proposed task scheduler consists of an on-line allocation strategy and a new voltage-scaling strategy. In particular, the proposed on-line allocation strategy uses the temperature-variation rates of the cores and takes into two important thermal behaviors of 3D-MCPs that can effectively minimize occurrences of hotspots in both thermally homogeneous and heterogeneous 3D-MCPs. Furthermore, a new vertical-grouping voltage scaling (VGVS) strategy that considers thermal correlation in 3D-MCPs is used to handle thermal emergencies. Experimental results indicate that, when compared to a previous online thermally constrained task scheduler, the proposed task scheduler can reduce hotspot occurrences by approximately 66% (71%) and improve throughput by approximately 8% (2%) in thermally homogeneous (heterogeneous) 3D-MCPs. These results indicate that the proposed task scheduler is an effective technique for suppressing hotspot occurrences and optimizing throughput for 3D-MCPs subject to thermal constraints.

As application hardware designs and implementations in a short term are required, high-level synthesis is more and more essential EDA technique nowadays. In deep-submicron era, interconnection delays are not negligible even in high-level synthesis thus distributed-register and -controller architectures (DR architectures) have been proposed in order to cope with this problem. It is also profitable to take data-bitwidth into account in high-level synthesis. In this paper, we propose a bitwidth-aware high-level synthesis algorithm using operation chainings targeting Tiled-DR architectures. Our proposed algorithm optimizes bitwidths of functional units and utilizes the vacant tiles by adding some extra functional units to realize effective operation chainings to generate high performance circuits without increasing the total area. Experimental results show that our proposed algorithm reduces the overall latency by up to 47% compared to the conventional approach without area overheads by eliminating unnecessary bitwidths and adding efficient extra FUs for Tiled-DR architectures.

This letter describes the development and implementation of the lane detection system accelerated by the neuromorphic hardware. Because the neuromorphic hardware has inherently parallel nature and has constant output latency regardless the size of the knowledge, the proposed lane detection system can recognize various types of lanes quickly and efficiently. Experimental results using the road images obtained in the actual driving environments showed that white and yellow lanes could be detected with an accuracy of more than 94 percent.

This paper presents convex filter networks that are obtained from extensions of morphological filters. The proposed filter network consists of a convex and concave filter that are extensions of the dilation and erosion of mathematical morphology with the maxout activation function. Maxout can approximate arbitrary convex functions as piecewise linear functions, including the max function. The class of the convex function hence includes the morphological dilation and can be trained for specific image processing tasks. In this paper, the closing filter is extended to a convex-concave filter network with maxout. The convex-concave filter is trained by the stochastic gradient method for noise and mask removal. The examples of noise and mask removal show that the convex-concave filter can obtain a recovered image, whose quality is comparable to inpainting by using the total variation minimization with reduced computational cost without mask information of the corrupted pixels.

This paper reports on the relationships between the performance of retro-reflectors and the sharpness of an aerial image formed with aerial imaging by retro-reflection (AIRR). We have measured the retro-reflector divergence angle and evaluated aerial image sharpness by use of the contrast-transfer function. It is found that the divergence angle of the retro-reflected light is strongly related to the sharpness of the aerial image formed with AIRR.

Massive amounts of computation involved in real-time evaluation of deep neural networks pose a serious challenge in battery-powered systems, and neuromorphic systems specialized in neural networks have been developed. This paper first shows the portion of active neurons at a time dwindles as going toward the output layer in recent large-scale deep convolutional neural networks. Spike-based, asynchronous neuromorphic systems take advantage of the sparse activation and reduce dynamic power consumption, while synchronous systems may waste much dynamic power even for the sparse activation due to clocks. We thus propose a clock gating-based dynamic power reduction method that exploits the sparse activation for synchronous neuromorphic systems. We apply the proposed method to a building block of a recently proposed synchronous neuromorphic computing system and demonstrate up to 79% dynamic power saving at a negligible overhead.

We propose a novel structure that can reduce the power consumption and extend the transmission distance of an electro-absorption modulator integrated with a DFB (EADFB) laser. To overcome the trade-off relationship of the optical loss and chirp parameter of the EA modulator, we integrate a semiconductor optical amplifier (SOA) with an EADFB laser. With the proposed SOA assisted extended reach EADFB laser (AXEL) structure, the LD and SOA sections are operated by an electrically connected input port. We describe a design for AXEL that optimizes the LD and SOA length ratio when their total operation current is 80mA. By using the designed AXEL, the power consumption of a 10-Gbit/s, 1.55-µm EADFB laser is reduced by 1/2 and at the same time the transmission distance is extended from 80 to 100km.

We build a privacy-preserving system of linear regression protecting both input data secrecy and output privacy. Our system achieves those goals simultaneously via a novel combination of homomorphic encryption and differential privacy dedicated to linear regression and its variants (ridge, LASSO). Our system is proved scalable over cloud servers, and its efficiency is extensively checked by careful experiments.

In this study, we focus on a method to search for similar trajectories. In the majority of previous works on searching for similar trajectories, only raw trajectory data were used. However, to obtain deeper insights, additional time-dependent trajectory features should be utilized depending on the search intent. For instance, to identify similar combination plays in soccer games, such additional features include the movements of the team players. In this paper, we develop a framework to flexibly search for similar trajectories associated with time-dependent features, which we call enriched trajectories. In this framework, weights, which represent the relative importance of each feature, can be flexibly given by users. Moreover, to facilitate fast searching, we first propose a lower bounding measure of the DTW distance between enriched trajectories, and then we propose algorithms based on this lower bounding measure. We evaluate the effectiveness of the lower bounding measure and compare the performances of the algorithms under various conditions using soccer data and synthetic data. Our experimental results suggest that the proposed lower bounding measure is superior to the existing measure, and one of the proposed algorithms, which is based on the threshold algorithm, is suitable for practical use.

Web tracking is widely used as a means to track user's behavior on websites. While web tracking provides new opportunities of e-commerce, it also includes certain risks such as privacy infringement. Therefore, analyzing such risks in the wild Internet is meaningful to make the user's privacy transparent. This work aims to understand how the web tracking has been adopted to prominent websites. We also aim to understand their resilience to the ad-blocking techniques. Web tracking-enabled websites collect the information called the web browser fingerprints, which can be used to identify users. We develop a scalable system that can detect fingerprinting by using both dynamic and static analyses. If a tracking site makes use of many and strong fingerprints, the site is likely resilient to the ad-blocking techniques. We also analyze the connectivity of the third-party tracking sites, which are linked from multiple websites. The link analysis allows us to extract the group of associated tracking sites and understand how influential these sites are. Based on the analyses of 100,000 websites, we quantify the potential risks of the web tracking-enabled websites. We reveal that there are 226 websites that adopt fingerprints that cannot be detected with the most of off-the-shelf anti-tracking tools. We also reveal that a major, resilient third-party tracking site is linked to 50.0 % of the top-100,000 popular websites.

Named Entity Translation Equivalents extraction plays a critical role in machine translation (MT) and cross language information retrieval (CLIR). Traditional methods are often based on large-scale parallel or comparable corpora. However, the applicability of these studies is constrained, mainly because of the scarcity of parallel corpora of the required scale, especially for language pairs of Chinese and Japanese. In this paper, we propose a method considering the characteristics of Chinese and Japanese to automatically extract the Chinese-Japanese Named Entity (NE) translation equivalents based on inductive learning (IL) from monolingual corpora. The method adopts the Chinese Hanzi and Japanese Kanji Mapping Table (HKMT) to calculate the similarity of the NE instances between Japanese and Chinese. Then, we use IL to obtain partial translation rules for NEs by extracting the different parts from high similarity NE instances in Chinese and Japanese. In the end, the feedback processing updates the Chinese and Japanese NE entity similarity and rule sets. Experimental results show that our simple, efficient method, which overcomes the insufficiency of the traditional methods, which are severely dependent on bilingual resource. Compared with other methods, our method combines the language features of Chinese and Japanese with IL for automatically extracting NE pairs. Our use of a weak correlation bilingual text sets and minimal additional knowledge to extract NE pairs effectively reduces the cost of building the corpus and the need for additional knowledge. Our method may help to build a large-scale Chinese-Japanese NE translation dictionary using monolingual corpora.