Department of Computer Science & Engineering

&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

Convolutional Neural networks(CNN) are one of the most powerful tools in the present era of science. There has been a lot of research done to improve their performance and robustness while their internal working was left unexplored to much extent. They are often defined as black boxes that can map non-linear data effectively. This paper answers the question, 'How does a CNN look at an image?'. Visual results are also provided to strongly support the proposed method. The proposed algorithm exploits the basic math behind CNN to backtrack the important pixels. This is a generic approach which can be applied to any architecture of a neural network. This doesn't require any additional training or architectural changes. In literature, few attempts have been made to explain how learning happens in CNN internally, by exploiting the convolution filter maps. This is a simple algorithm as it does not involve any cost functions, filter exploitation, gradient calculations or probability scores. Further, we demonstrate that the proposed scheme can be used in some important computer vision tasks such as object detection, salient region proposal, etc. © 2019 IEEE.

World University root node

IEEE Region 10 Annual International Conference, Proceedings/TENCON

How You See Me: Understanding Convolutional Neural Networks

Floating point multiplication plays a crucial role in computationally intensive applications like digital signal processing. This paper deals with the design of a single precision floating point multiplier and its FPGA realization with LCD interface for output display. To bring out the need for secure hardware design, hardware Trojan models are proposed for the mantissa multiplication unit of the floating point multiplier. Implementation results show that the Trojans produce an average difference of 15%-20% in the product values, an increase of on-chip power by 1.61% and an increase of 0.4% in the number of LUTs. The negligible change in the area and power dissipated establishes the stealthy nature of the proposed Trojans. © 2019 IEEE.

2019 IEEE 16th India Council International Conference, INDICON 2019 - Symposium Proceedings

FPGA based implementation of a floating point multiplier and its hardware trojan models

In this work, we propose an end-to-end trainable model of Generative Adversarial Networks (GAN) which is engineered to hide audio data in images. Due to the non-stationary property of audio signals and lack of powerful tools, audio hiding in images was not explored well. We devised a deep generative model that consists of an auto-encoder as generator along with one discriminator that are trained to embed the message while, an exclusive extractor network with an audio discriminator is trained fundamentally to extract the hidden message from the encoded host signal. The encoded image is subjected to few common attacks and it is established that the message signal can not be hindered making the proposed method robust towards blurring, rotation, noise, and cropping. The one remarkable feature of our method is that it can be trained to recover against various attacks and hence can also be used for watermarking. © 2019, Springer Nature Switzerland AG.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Hiding Audio in Images: A Deep Learning Approach

Detecting stellar clusters have always been an important research problem in Astronomy. Although images do not convey very detailed information in detecting stellar density enhancements, we attempt to understand if new machine learning techniques can reveal patterns that would assist in drawing better inferences from the available image data. This paper describes an unsupervised approach in detecting star clusters using Deep Variational Autoencoder combined with a Gaussian Mixture Model. We show that our method works significantly well in comparison with state-of-the-art detection algorithm in recognizing a variety of star clusters even in the presence of noise and distortion. © 2018 IEEE.

2018 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2018

Stellar cluster detection using GMM with deep variational autoencoder

Navigation is an indispensable part of everyday human life. In recent times, satellite based navigation system has proven to be indispensable in various navigation and scientific applications [1]. The backbone of satellite based navigation systems is very precise and highly stable atomic clocks. Satellite-based and ground-based clocks formulate the complete operability of a navigation system. The satellite-based clocks fall under the category of portable and space qualified atomic frequency standards. For the applications that demand to address the issues of performance, portability, reliability, cost, weight, volume and power consumption, the Rubidium Atomic Frequency Standards (RAFS) are advantageous over other kinds of clocks [2, 3]. These characteristics makes it is a suitable candidate for portable and onboard navigation payload applications such as Global Navigation Satellite Systems (GNSS). At the Space Applications Centre, we are indigenously designing and developing the RAFS for Navigation with Indian Constellation (NavIC) [previously known as Indian Regional Navigation Satellite System (IRNSS)]. © 2019 URSI. All rights reserved.

2019 URSI Asia-Pacific Radio Science Conference, AP-RASC 2019

Indian Rubidium Atomic Frequency Standard (IRAFS) development for satellite navigation

Face recognition systems are vulnerable to presentation attacks such as replay and 3D masks. In the literature, several presentation attack detection (PAD) algorithms are developed to address this problem. However, for the first time in the literature, this paper showcases that it is possible to "fool" the PAD algorithms using adversarial perturbations. The proposed perturbation approach attacks the presentation attack detection algorithms at the PAD feature level via transformation of features from one class (attack class) to another (real class). The PAD feature tampering network utilizes convolutional autoencoder to learn the perturbations. The proposed algorithm is evaluated with respect to CNN and local binary pattern (LBP) based PAD algorithms. Experiments on three databases, Replay, SMAD, and Face Morph, showcase that the proposed approach increases the equal error rate of PAD algorithms by at least two times. For instance, on the SMAD database, PAD equal error rate (EER) of 20.1% is increased to 55.7% after attacking the PAD algorithm. © 2019 IEEE.

2019 International Conference on Biometrics, ICB 2019

Deceiving the Protector: Fooling Face Presentation Attack Detection Algorithms

Dropout is often used in deep neural networks to prevent over-fitting. Conventionally, dropout training invokes random drop of nodes from the hidden layers of a Neural Network. It is our hypothesis that a guided selection of nodes for intelligent dropout can lead to better generalization as compared to the traditional dropout. In this research, we propose “guided dropout” for training deep neural network which drop nodes by measuring the strength of each node. We also demonstrate that conventional dropout is a specific case of the proposed guided dropout. Experimental evaluation on multiple datasets including MNIST, CIFAR10, CIFAR100, SVHN, and Tiny ImageNet demonstrate the efficacy of the proposed guided dropout. © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org).

33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019

Guided dropout

Face sketch recognition is one of the most challenging heterogeneous face recognition problems. The large domain difference of hand-drawn sketches and color photos along with the subjectivity/variations due to eye-witness descriptions and skill of sketch artists makes the problem demanding. Therefore, despite several research attempts, sketch to photo matching is still considered an arduous problem. In this research, we propose to transform a hand-drawn sketch to a color photo using an end to end two-stage generative adversarial model followed by learning a discriminative classifier for matching the transformed images with color photos. The proposed image to image transformation model reduces the modality gap of the sketch images and color photos resulting in higher identification accuracies and images with better visual quality than the ground truth sketch images. © 2019 IEEE.

Face Sketch Colorization via Supervised GANs

IIITD-Delhi

KinshipGAN: Generating Kin Images

Developing a model for the task of assessing quality of human action is a key research area in computer vision. The quality assessment task has been posed as a supervised regression problem, where models have been trained to predict score, given action representation features. However, human proficiency levels can widely vary and so do their scores. Providing all such performance variations and their respective scores is an expensive solution as it requires a domain expert to annotate many videos. The question arises - Can we exploit the variations of the performances from that of expert and map the variations to their respective scores? To this end, we introduce a novel sequence-to-sequence autoencoder-based scoring model which learns the representation from only expert performances and judges an unknown performance based on how well it can be regenerated from the learned model. We evaluated our model in predicting scores of a complex Sun- Salutation action sequence, and demonstrate that our model gives remarkable prediction accuracy compared to the baselines. © 2019 IEEE.