Improving Document Binarization via Adversarial Noise-Texture Augmentation

Binarization of degraded document images is an elementary step in most of the problems in document image analysis domain. The paper re-visits the binarization problem by introducing an adversarial learning approach. We construct a Texture Augmentation Network that transfers the texture element of a degraded reference document image to a clean binary image. In this way, the network creates multiple versions of the same textual content with various noisy textures, thus enlarging the available document binarization datasets. At last, the newly generated images are passed through a Binarization network to get back the clean version. By jointly training the two networks we can increase the adversarial robustness of our system. Also, it is noteworthy that our model can learn from unpaired data. Experimental results suggest that the proposed method achieves superior performance over widely used DIBCO datasets.Comment: IEEE International Conference on Image Processing (ICIP), 2019. The full source code of the proposed system is publicly available at https://github.com/ankanbhunia/AdverseBiNe

Twisted conjugacy in linear algebraic groups

Let $k$ be an algebraically closed field, $G$ a linear algebraic group over $k$ and $\varphi\in Aut(G)$, the group of all algebraic group automorphisms of $G$. Two elements $x, y$ of $G$ are said to be $\varphi$-twisted conjugate if $y=gx\varphi(g)^{-1}$ for some $g\in G$. In this paper we prove that for a connected non-solvable linear algebraic group $G$ over $k$, the number of its $\varphi$-twisted conjugacy classes is infinite for every $\varphi\in Aut(G)$.Comment: 15 pages, 1 figure. Some proofs are rewritten. Final version to appear in Transformation Group

A Deep One-Shot Network for Query-based Logo Retrieval

Logo detection in real-world scene images is an important problem with applications in advertisement and marketing. Existing general-purpose object detection methods require large training data with annotations for every logo class. These methods do not satisfy the incremental demand of logo classes necessary for practical deployment since it is practically impossible to have such annotated data for new unseen logo. In this work, we develop an easy-to-implement query-based logo detection and localization system by employing a one-shot learning technique. Given an image of a query logo, our model searches for it within a given target image and predicts the possible location of the logo by estimating a binary segmentation mask. The proposed model consists of a conditional branch and a segmentation branch. The former gives a conditional latent representation of the given query logo which is combined with feature maps of the segmentation branch at multiple scales in order to find the matching position of the query logo in a target image, should it be present. Feature matching between the latent query representation and multi-scale feature maps of segmentation branch using simple concatenation operation followed by 1x1 convolution layer makes our model scale-invariant. Despite its simplicity, our query-based logo retrieval framework achieved superior performance in FlickrLogos-32 and TopLogos-10 dataset over different existing baselines.Comment: Accepted in Pattern Recognition, Elsevier(2019

Indic Handwritten Script Identification using Offline-Online Multimodal Deep Network

In this paper, we propose a novel approach of word-level Indic script identification using only character-level data in training stage. The advantages of using character level data for training have been outlined in section I. Our method uses a multimodal deep network which takes both offline and online modality of the data as input in order to explore the information from both the modalities jointly for script identification task. We take handwritten data in either modality as input and the opposite modality is generated through intermodality conversion. Thereafter, we feed this offline-online modality pair to our network. Hence, along with the advantage of utilizing information from both the modalities, it can work as a single framework for both offline and online script identification simultaneously which alleviates the need for designing two separate script identification modules for individual modality. One more major contribution is that we propose a novel conditional multimodal fusion scheme to combine the information from offline and online modality which takes into account the real origin of the data being fed to our network and thus it combines adaptively. An exhaustive experiment has been done on a data set consisting of English and six Indic scripts. Our proposed framework clearly outperforms different frameworks based on traditional classifiers along with handcrafted features and deep learning based methods with a clear margin. Extensive experiments show that using only character level training data can achieve state-of-art performance similar to that obtained with traditional training using word level data in our framework.Comment: Accepted in Information Fusion, Elsevie

Modeling and Analysis of Loading Effect in Leakage of Nano-Scaled Bulk-CMOS Logic Circuits

In nanometer scaled CMOS devices significant increase in the subthreshold, the gate and the reverse biased junction band-to-band-tunneling (BTBT) leakage, results in the large increase of total leakage power in a logic circuit. Leakage components interact with each other in device level (through device geometry, doping profile) and also in the circuit level (through node voltages). Due to the circuit level interaction of the different leakage components, the leakage of a logic gate strongly depends on the circuit topology i.e. number and nature of the other logic gates connected to its input and output. In this paper, for the first time, we have analyzed loading effect on leakage and proposed a method to accurately estimate the total leakage in a logic circuit, from its logic level description considering the impact of loading and transistor stacking.Comment: Submitted on behalf of EDAA (http://www.edaa.com/

Bounds of numerical radius of bounded linear operator using tt-Aluthge transform

We develop a number of inequalities to obtain bounds for the numerical radius of a bounded linear operator defined on a complex Hilbert space using the properties of $t$-Aluthge transform. We show that the bounds obtained are sharper than the existing bounds.Comment: 14 page

z-Classes and Rational Conjugacy Classes in Alternating Groups

In this paper, we compute the number of z-classes (conjugacy classes of centralizers of elements) in the symmetric group S_n, when n is greater or equal to 3 and alternating group A_n, when n is greater or equal to 4. It turns out that the difference between the number of conjugacy classes and the number of z-classes for S_n is determined by those restricted partitions of n-2 in which 1 and 2 do not appear as its part. And, in the case of alternating groups, it is determined by those restricted partitions of n-3 which has all its parts distinct, odd and in which 1 (and 2) does not appear as its part, along with an error term. The error term is given by those partitions of n which have each of its part distinct, odd and perfect square. Further, we prove that the number of rational-valued irreducible complex characters for A_n is same as the number of conjugacy classes which are rational.Comment: 20 page

SAIL: Machine Learning Guided Structural Analysis Attack on Hardware Obfuscation

Obfuscation is a technique for protecting hardware intellectual property (IP) blocks against reverse engineering, piracy, and malicious modifications. Current obfuscation efforts mainly focus on functional locking of a design to prevent black-box usage. They do not directly address hiding design intent through structural transformations, which is an important objective of obfuscation. We note that current obfuscation techniques incorporate only: (1) local, and (2) predictable changes in circuit topology. In this paper, we present SAIL, a structural attack on obfuscation using machine learning (ML) models that exposes a critical vulnerability of these methods. Through this attack, we demonstrate that the gate-level structure of an obfuscated design can be retrieved in most parts through a systematic set of steps. The proposed attack is applicable to all forms of logic obfuscation, and significantly more powerful than existing attacks, e.g., SAT-based attacks, since it does not require the availability of golden functional responses (e.g. an unlocked IC). Evaluation on benchmark circuits show that we can recover an average of around 84% (up to 95%) transformations introduced by obfuscation. We also show that this attack is scalable, flexible, and versatile.Comment: 6 pages, 6 figures, 8 table

Numerical radius inequalities and its applications in estimation of zeros of polynomials

We present some upper and lower bounds for the numerical radius of a bounded linear operator defined on complex Hilbert space, which improves on the existing upper and lower bounds. We also present an upper bound for the spectral radius of sum of product of $n$ pairs of operators. As an application of the results obtained, we provide a better estimation for the zeros of a given polynomial

Numerical radius inequalities of operator matrices with applications

We present upper and lower bounds for the numerical radius of $2 \times 2$ operator matrices which improves on the existing bound for the same. As an application of the results obtained we give a better estimation for the zeros of a polynomial