Sei Nagashima

High-resolution side-channel attack using phase-based waveform matching

This paper describes high-resolution waveform matching based on a Phase-Only Correlation (POC) technique and its application for a side-channel attack. Such attacks, such as Simple Power Analysis (SPA) and Differential Power Analysis (DPA), use a statistical analysis of signal waveforms (e.g., power traces) to reduce noise and to retrieve secret information. However, the waveform data often includes displacement errors in the measurements. The use of phase components in the discrete Fourier transforms of the waveforms makes it possible to estimate the displacements between the signal waveforms with higher resolution than the sampling resolution. The accuracy of a side-channel attack can be enhanced using this high-resolution matching method. In this paper, we demonstrate the advantages of the POC-based method in comparison with conventional approaches through experimental DPA and Differential ElectroMagnetic Analysis (DEMA) against a DES software implementation on a Z80 processor.

A Subpixel Image Matching Technique Using Phase-Only Correlation

This paper presents a high-accuracy image matching technique using a phase-only correlation (POC) function. The POC-based image matching enables estimation of image displacements with 1/10~1/100-pixel accuracy by a function fitting technique using the closed-form representation of the POC functions peak. This method requires an iterative process for the nonlinear function fitting, resulting in long computation times. In this paper, we propose a peak evaluation formula (PEF) that directly estimates the correlation peak location from actual 2-D data array of the POC function. Experimental evaluation shows that the proposed method reduces computation time without sacrificing image matching accuracy

DPA Using Phase-Based Waveform Matching against Random-Delay Countermeasure

We propose differential power analysis (DPA) with a phase-based waveform matching technique. Conventionally, a trigger signal and a system clock are used to capture the waveform traces, but the signals always contain jitter-related deviations, and this degrades the accuracy of the statistical analysis. Our method can adjust for this timing deviation with a higher resolution than the sampling rate by post-processing on the measured waveforms. Therefore, no modification of the measuring equipment is required. Our method can also defeat DPA countermeasures creating distorted waveforms with random delays or dummy cycles. We implemented Data Encryption Standard (DES) software with and without the countermeasure on a Z80 microprocessor, and demonstrated the advantages of our method in comparison with a conventional attack.

A High-Resolution Phase-Based Waveform Matching and Its Application to Side-Channel Attacks

This paper presents an enhanced side-channel attack using a phase-based waveform matching technique. Conventionally, side-channel attacks such as Simple Power Analysis (SPA) and Differential Power Analysis (DPA) capture signal waveforms (e.g., power traces) with a trigger signal or a system clock, and use a statistical analysis of the waveforms to reduce noise and to retrieve secret information. However, the waveform data often includes displacement errors, and this degrades the accuracy of the statistical analysis. The use of a Phase-Only Correlation (POC) technique makes it possible to estimate the displacements between the signal waveforms with higher resolution than the sampling resolution. The accuracy of side-channel attacks can be enhanced using the POC-based matching method. Also, a popular DPA countermeasure of creating distorted waveforms with random delays can be defeated by our method. In this paper, we demonstrate the advantages of the proposed method in comparison with conventional approaches of experimental DPA and Differential ElectroMagnetic Analysis (DEMA) against DES software and hardware implementations.

Performance improvement of face recognition algorithms using occluded-region detection

Facial occlusions such as eyeglasses, hairs and beards decrease the performance of face recognition algorithms. To improve the performance of face recognition algorithms, this paper proposes a novel framework of face recognition combined with the occluded-region detection method. In this paper, we detect occluded regions using Fast-Weighted Principal Component Analysis (FW-PCA) and use the occluded regions as weights for matching face images. To demonstrate the effectiveness of the proposed framework, we use two face recognition algorithms: Local Binary Patterns (LBP) and Phase-Only Correlation (POC). Experimental evaluation using public face image databases indicates performance improvement of the face recognition algorithms for face images with natural and artificial occlusions.

Restoring occluded regions using FW-PCA for face recognition

Occlusions in face images such as eyeglasses, hairs and whiskers decrease the performance of face recognition algorithms. Addressing this problem, this paper proposes a method for restoring occluded regions in face images. The proposed method employs Fast Weighted Principal Component Analysis (FW-PCA), which computes PCA only with effective pixels. The use of FW-PCA makes it possible to detect and restore occluded regions in face images. Through a set of experiments using public face databases, we demonstrate the effectiveness of the proposed method compared with the conventional methods.

A high-accuracy rotation estimation algorithm based on 1D phase-only correlation

This paper proposes a high-accuracy rotation estimation algorithm using 1D Phase-Only Correlation (POC). In general, the rotation angle between two images is estimated as follows: (i) convert the image rotation into the image shift by polar mappings of the amplitude spectra of images, and (ii) estimate the translational displacement between the polar mappings to obtain the rotation angle. The problem of rotation estimation between two images is replaced to 1D displacement estimation between pairs of horizontal lines at the same vertical position in two polar mappings. The proposed algorithm employs 1D POC instead of 2D matching for estimating a rotation angle. The use of 1D POC to estimate the rotation angle makes it possible to reduce the computational cost significantly without sacrificing the estimation accuracy.

Reconstructing occluded regions using fast weighted PCA

Reconstructing occluded regions of the object is to automatically detect the occluded regions and background in the image and reconstruct these regions using image interpolation. This paper proposes a novel occluded region reconstruction method using Fast Weighted Principal Component Analysis (FW-PCA). The computation time of the weighted PCA can be reduced by using only the effective regions when calculating the principal component scores. The occluded regions are accurately detected by recursively updating the weight for each pixel in the image using FW-PCA. Then, the occluded regions can be reconstructed using the final weight. Thorough a set of experiments, we demonstrate that the proposed method exhibits higher performance than the conventional method.

Performance evaluation using Mandelbrot images for image registration algorithms

High-accuracy image registration is an important fundamental task in many fields, such as image sensing, image/video processing, computer vision, etc. In order to evaluate accuracy of image registration algorithms, the reference images transformed with known parameters have to be used. Reference images taken by a camera may include human errors, while reference images generated by a computer may require pixel interpolation in the process. To address these problems, this paper proposes a performance evaluation method using the Mandelbrot set which is one of the famous fractals. Experimental evaluation shows effectiveness of the proposed method.