The detection and recognition of objects in images is a key research topic in the computer vision community Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection It is also of interest to graduate students undertaking studies in these areas

Template Matching Techniques in Computer Vision: Theory and Practice

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Template Matching Techniques in Computer Vision

In the context of the security evaluation of cryptographic implementations, profiling attacks (aka Template Attacks) play a fundamental role. Nowadays the most popular Template Attack strategy consists in approximating the information leakages by Gaussian distributions. Nevertheless this approach suffers from the difficulty to deal with both the traces misalignment and the high dimensionality of the data. This forces the attacker to perform critical preprocessing phases, such as the selection of the points of interest and the realignment of measurements. Some software and hardware countermeasures have been conceived exactly to create such a misalignment. In this paper we propose an end-to-end profiling attack strategy based on the Convolutional Neural Networks: this strategy greatly facilitates the attack roadmap, since it does not require a previous trace realignment nor a precise selection of points of interest. To significantly increase the performances of the CNN, we moreover propose to equip it with the data augmentation technique that is classical in other applications of Machine Learning. As a validation, we present several experiments against traces misaligned by different kinds of countermeasures, including the augmentation of the clock jitter effect in a secure hardware implementation over a modern chip. The excellent results achieved in these experiments prove that Convolutional Neural Networks approach combined with data augmentation gives a very efficient alternative to the state-of-the-art profiling attacks.

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Convolutional Neural Networks with Data Augmentation Against Jitter-Based Countermeasures

Viable cryptosystem designs must address power analysis attacks, and masking is a commonly proposed technique for defending against these side-channel attacks It is possible to overcome simple masking by using higher-order techniques, but apparently only at some cost in terms of generality, number of required samples from the device being attacked, and computational complexity We make progress towards ascertaining the significance of these costs by exploring a couple of attacks that attempt to efficiently employ second-order techniques to overcome masking In particular, we consider two variants of second-order differential power analysis: Zero-Offset 2DPA and FFT 2DPA

Towards efficient second-order power analysis

Random delays are a countermeasure against a range of side channel and fault attacks that is often implemented in embedded software. We propose a new method for generation of random delays and a criterion for measuring the efficiency of a random delay countermeasure. We implement this new method along with the existing ones on an 8-bit platform and mount practical side-channel attacks against the implementations. We show that the new method is significantly more secure in practice than the previously published solutions and also more lightweight.

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An Efficient Method for Random Delay Generation in Embedded Software

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.

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

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 function's 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

A Subpixel Image Matching Technique Using Phase-Only Correlation

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.

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

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.

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

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.

Sei Nagashima

Papers

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

A Subpixel Image Matching Technique Using Phase-Only Correlation

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

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

Restoring occluded regions using FW-PCA for face recognition