MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense

Present attack methods can make state-of-the-art classification systems based on deep neural networks misclassify every adversarially modified test example. The design of general defense strategies against a wide range of such attacks still remains a challenging problem. In this paper, we draw inspiration from the fields of cybersecurity and multi-agent systems and propose to leverage the concept of Moving Target Defense (MTD) in designing a meta-defense for 'boosting' the robustness of an ensemble of deep neural networks (DNNs) for visual classification tasks against such adversarial attacks. To classify an input image, a trained network is picked randomly from this set of networks by formulating the interaction between a Defender (who hosts the classification networks) and their (Legitimate and Malicious) users as a Bayesian Stackelberg Game (BSG). We empirically show that this approach, MTDeep, reduces misclassification on perturbed images in various datasets such as MNIST, FashionMNIST, and ImageNet while maintaining high classification accuracy on legitimate test images. We then demonstrate that our framework, being the first meta-defense technique, can be used in conjunction with any existing defense mechanism to provide more resilience against adversarial attacks that can be afforded by these defense mechanisms. Lastly, to quantify the increase in robustness of an ensemble-based classification system when we use MTDeep, we analyze the properties of a set of DNNs and introduce the concept of differential immunity that formalizes the notion of attack transferability.Comment: Accepted to the Conference on Decision and Game Theory for Security (GameSec), 201

Pharmaceutico-Analytical Study of Agnikumara Rasa - A Kupipakwa Kalpana

Agnikumara Rasa is a Sagandha, Sagni, Kantastha Bahirdhuma Kupipakwa Rasayana mentioned in Rasakamadhenu under Sangrahani Chikitsa Adhikara. It is prepared under Kramagni Tapa for 18 hours as per classics. The core ingredients are Shuddha Parada, Shuddha Gandhaka, Shuddha Vatsanabha and Hamsapadi Swarasa. It is indicated in conditions like Sannipata Kasa, Shwasa, Kshaya, Panduroga and Mandagni. Even though a total of 50 formulations have been explained in classics under the name of Agnikumara Rasa, no research work has been done till date on this particular yoga explained in Rasakamadhenu

Modeling Frictional Characteristics of Water Flowing Through Microchannel

The present study aims at modeling the real random rough surface of a microchannel with structured rough channel of known geometric parameters. The surface of the microchannel is created by sinusoidal function using MATLAB code and 2D simulation of the model is carried out with commercial software ANSYS Fluent. The height of the channel is varied from 100 to 250 µm and length of the channel is 12.5 mm. The range of Reynolds number selected for analysis is 100 to 500 with water as the fluid medium. The roughness height is selected within the range of actual manufacturing roughness level of microchannels. The results show that channel geometry has significant influence on flow characteristics. A new non-dimensional roughness parameter β, is proposed to represent the dependence of friction factor on geometric parameters in the laminar region. A correlation for flow friction is developed as a function of roughness parameter and Reynolds number

Insights into the function of silver as an oxidation catalyst by ab initio, atomistic thermodynamics

To help understand the high activity of silver as an oxidation catalyst, e.g., for the oxidation of ethylene to epoxide and the dehydrogenation of methanol to formaldehyde, the interaction and stability of oxygen species at the Ag(111) surface has been studied for a wide range of coverages. Through calculation of the free energy, as obtained from density-functional theory and taking into account the temperature and pressure via the oxygen chemical potential, we obtain the phase diagram of O/Ag(111). Our results reveal that a thin surface-oxide structure is most stable for the temperature and pressure range of ethylene epoxidation and we propose it (and possibly other similar structures) contains the species actuating the catalysis. For higher temperatures, low coverages of chemisorbed oxygen are most stable, which could also play a role in oxidation reactions. For temperatures greater than about 775 K there are no stable oxygen species, except for the possibility of O atoms adsorbed at under-coordinated surface sites Our calculations rule out thicker oxide-like structures, as well as bulk dissolved oxygen and molecular ozone-like species, as playing a role in the oxidation reactions.Comment: 15 pages including 9 figures, Related publications can be found at http://www.fhi-berlin.mpg.de/th/paper.htm

On the inadequacy of environment impact assessments for projects in Bhagwan Mahavir Wildlife Sanctuary and National Park of Goa, India : a peer review

The Environment Impact Assessment (EIA) is a regulatory framework adopted since 1994 in India to evaluate the impact and mitigation measures of projects, however, even after 25 years of adoption, EIAs continue to be of inferior quality with respect to biodiversity documentation and assessment of impacts and their mitigation measures. This questions the credibility of the exercise, as deficient EIAs are habitually used as a basis for project clearances in ecologically sensitive and irreplaceable regions. The authors reiterate this point by analysing impact assessment documents for three projects: the doubling of the National Highway-4A, doubling of the railway-line from Castlerock to Kulem, and laying of a 400-kV transmission line through the Bhagwan Mahavir Wildlife Sanctuary and National Park in the state of Goa. Two of these projects were recently granted ‘Wildlife Clearance’ during a virtual meeting of the Standing Committee of the National Board of Wildlife (NBWL) without a thorough assessment of the project impacts. Assessment reports for the road and railway expansion were found to be deficient on multiple fronts regarding biodiversity assessment and projected impacts, whereas no impact assessment report was available in the public domain for the 400-kV transmission line project. This paper highlights the biodiversity significance of this protected area complex in the Western Ghats, and highlights the lacunae in biodiversity documentation and inadequacy of mitigation measures in assessment documents for all three diversion projects. The EIA process needs to improve substantially if India is to protect its natural resources and adhere to environmental protection policies and regulations nationally and globally

Dying in Original Sin Vis-à-vis Living in Disgrace—In Defense of the Right to Socio-Eugenic Abortion as Personal Liberty

Abstrac

Handwriting recognition in indian regional scripts: A survey of offline techniques

Offline handwriting recognition in Indian regional scripts is an interesting area of research as almost 460 million people in India use regional scripts. The nine major Indian regional scripts are Bangla (for Bengali and Assamese languages), Gujarati, Kannada, Malayalam, Oriya, Gurumukhi (for Punjabi language), Tamil, Telugu, and Nastaliq (for Urdu language). A state-of-the-art survey about the techniques available in the area of offline handwriting recognition (OHR) in Indian regional scripts will be of a great aid to the researchers in the subcontinent and hence a sincere attempt is made in this article to discuss the advancements reported in this regard during the last few decades. The survey is organized into different sections. A brief introduction is given initially about automatic recognition of handwriting and official regional scripts in India. The nine regional scripts are then categorized into four subgroups based on their similarity and evolution information. The first group contains Bangla, Oriya, Gujarati and Gurumukhi scripts. The second group contains Kannada and Telugu scripts and the third group contains Tamil and Malayalam scripts. The fourth group contains only Nastaliq script (Perso-Arabic script for Urdu), which is not an Indo-Aryan script. Various feature extraction and classification techniques associated with the offline handwriting recognition of the regional scripts are discussed in this survey. As it is important to identify the script before the recognition step, a section is dedicated to handwritten script identification techniques. A benchmarking database is very important for any pattern recognition related research. The details of the datasets available in different Indian regional scripts are also mentioned in the article. A separate section is dedicated to the observations made, future scope, and existing difficulties related to handwriting recognition in Indian regional scripts. We hope that this survey will serve as a compendium not only for researchers in India, but also for policymakers and practitioners in India. It will also help to accomplish a target of bringing the researchers working on different Indian scripts together. Looking at the recent developments in OHR of Indian regional scripts, this article will provide a better platform for future research activities. © 2012 ACM

Recognition of Devanagari Scene Text Using Autoencoder CNN

Scene text recognition is a well-rooted research domain covering a diverse application area. Recognition of scene text is challenging due to the complex nature of scene images. Various structural characteristics of the script also influence the recognition process. Text and background segmentation is a mandatory step in the scene text recognition process. A text recognition system produces the most accurate results if the structural and contextual information is preserved by the segmentation technique. Therefore, an attempt is made here to develop a robust foreground/background segmentation(separation) technique that produces the highest recognition results. A ground-truth dataset containing Devanagari scene text images is prepared for the experimentation. An encoder-decoder convolutional neural network model is used for text/background segmentation. The model is trained with Devanagari scene text images for pixel-wise classification of text and background. The segmented text is then recognized using an existing OCR engine (Tesseract). The word and character level recognition rates are computed and compared with other existing segmentation techniques to establish the effectiveness of the proposed technique