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

With the rapid growth of the World Wide Web and Internet of Things, a huge amount of digital data is being produced every day. Digital forensics investigators face an uphill task when they have to manually screen through and examine tons of such data during a crime investigation. To expedite this process, several automated techniques have been proposed and are being used in practice. Among which tools based on Approximate Matching algorithms have gained prominence, e.g., ssdeep, sdhash, mvHash etc. These tools produce hash signatures for all the files to be examined, compute a similarity score and then compare it with a known reference set to filter out known good as well as bad files. In this way, exact as well as similar matches can be screened out. However, all of these schemes have been shown to be prone to active adversary attack, whereby an attacker, by making feasible changes in the content of the file, intelligently modifies the final hash signature produced to evade detection. Thus, an alternate hashing scheme is required which can resist this attack. In this work, we propose a new Approximate Matching scheme termed as - FbHash. We show that our scheme is secure against active attacks and detects similarity with 98% accuracy. We also provide a detailed comparative analysis with other existing schemes and show that our scheme has a 28% higher accuracy rate than other schemes for uncompressed file format (e.g., text files) and 50% higher accuracy rate for compressed file format (e.g., docx etc.). Our proposed scheme is able to correlate a fragment as small as 1% to the source file with 100% detection rate and able to detect commonality as small as 1% between two documents with appropriate similarity score. Further, our scheme also produces the least false negatives in comparison to other schemes. © 2019

World University root node

Fulltext

Digital Investigation

FbHash: A New Similarity Hashing Scheme for Digital Forensics

Increasing threat of password leakage from compromised password hashes demands a resource consuming password-hashing algorithm to prevent the precomputation of the password hashes. A class of password-hashing schemes (PHS) provides such a defense by making the design Memory hard. This ensures that any reduction in the memory consumed by the algorithm leads to an exponential increase in its runtime. The security offered by a memory-hard PHS design is measured in terms of its time–memory trade-off (TMTO) defense. Another important measure for a good PHS is its efficiency in utilizing all the available memory as quickly as possible, and fast running time when more than the required memory is available. In this work, we present a simple technique to analyze TMTO for a password-hashing scheme which can be represented as a directed acyclic graph (DAG). The nodes of the DAG correspond to the storage required by the algorithm and the edges correspond to the flow of the execution. Our proposed technique provides expected runtimes at varied levels of available storage utilizing the DAG representation of the algorithm. We show the effectiveness of our proposed technique by applying it on three designs from the “Password Hashing Competition" (PHC)—Argon2-Version 1.2.1 (the PHC winner), Catena-Version 3.2 and Rig-Version 2. Our analysis shows that Argon2i is not providing expected memory hardness which is also highlighted in a recent work by Corrigan-Gibbs et al. We analyze these PHS for performance under various settings of time and memory complexities. Our experimental results show (i) simple DAGs for PHS are efficient but not memory hard, (ii) complex DAGs for PHS are memory hard but less efficient, and (iii) combination of two simple graphs in the representation of a DAG for PHS achieves both memory hardness and efficiency. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

International Journal of Information Security

Cryptanalytic time–memory trade-off for password hashing schemes

It is NP -hard to determine the minimum number of branching vertices needed in a single-source distance-preserving subgraph of an undirected graph. We show that this problem can be solved in polynomial time if the input graph is an interval graph. In earlier work, it was shown that every interval graph with k terminal vertices admits an all-pairs distance-preserving subgraph with O(klog k) branching verticesÂ [13]. We extend this result to bi-interval graphs; these are graphs that can be expressed as the strong product of two interval graphs. We present a polynomial time algorithm that takes a bi-interval graph with k terminal vertices as input, and outputs an all-pairs distance-preserving subgraph of it with O(k2) branching vertices. This bound is tight. © Springer Nature Switzerland AG 2019.

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

Minimizing branching vertices in distance-preserving subgraphs

This paper aims at developing a novel methodology for unsupervised video tracking by exploring the merits of neighborhood rough sets. A neighborhood rough filter is designed in this process for initial labeling of continuous moving object(s) even in the presence of several variations in different feature spaces. The locations and color models of the object(s) are estimated using their lower-upper approximations in spatio-color neighborhood granular space. Velocity neighborhood granules and acceleration neighborhood granules are then defined over this estimation to predict the object location in the next frame and to speed up the tracking process. A novel concept, namely, intuitionsistic entropy is introduced here, which consists of two new measures: neighborhood rough entropy and neighborhood probabilistic entropy to deal with the ambiguities that arise due to occurrence of overlapping/ occlusion in a video sequence. The unsupervised method of tracking is equally good even when compared with some of the state-of-the art partially supervised methods while showing superior performance during total occlusion. © 1993-2012 IEEE.

IEEE Transactions on Fuzzy Systems

Neighborhood rough filter and intuitionistic entropy in unsupervised tracking

This paper addresses the problem of Bayesian Block Sparse Modeling when coefficients within the blocks are correlated. In contrast to the current hierarchical methods which do not exploit correlation structure within the blocks, we propose a three level hierarchical estimation framework. It employs heavy-tailed priors for block sparse modeling and variational inference for Bayesian estimation. This paper also describes the relationship between proposed framework and some of the existing Block Sparse Bayesian Learning (SBL) methods and show that these SBL methods can be viewed as its special cases. Extensive experimental results for synthetic signals are provided, demonstrating the superior performance of the proposed framework in terms of failure rate, relative reconstruction error, to name a few. We also demonstrate the applicability of this framework in telemonitoring of Fetal Electrocardiogram. © 2018 IEEE.

Proceedings - International Conference on Pattern Recognition

Variational Bayes Block Sparse Modeling with Correlated Entries

Drought stress detection involves multi-modal image analysis with high spatio-temporal resolution. Identification of digital traits that characterizes drought stress response (DSR) is challenging due to high volume of image based features. Also, the labelled data that categorizes DSR are either unavailable or subjectively developed, which is a low-throughput and error-prone task. Therefore, we propose a novel framework that provides an automated scoring of DSR based on multi-trait fusion. k-means clustering was used to extract latent drought clusters and the relevant traits were identified using Support Vector Machine-Recursive Feature Extraction (SVM-RFE). Using these traits, SVM based DSR classification model was constructed. The framework has been validated on visible and thermal shoot images of rice plants, yielding 95% accuracy. Various imaging modalities can be integrated with the proposed framework, thus making it scalable as no prior information about the DSR was assumed. © Springer Nature Singapore Pte Ltd. 2018.

Communications in Computer and Information Science

Multi-modal image analysis for plant stress phenotyping

Biometric recognition of newborns, toddlers, and pre-school children aims is an important research challenge with applications in identifying newborn swapping, missing kids, and disbursing benefits. In this research, we propose a representation learning algorithm to extract unique and invariant features from face images of newborns and toddlers, to design an efficient face recognition algorithm. Specifically, we propose a deep learning model which applies class-based penalties while learning the filters of a convolutional neural network. The proposed CNN architecture achieves a rank-1 identification accuracy of 62.7% for single gallery newborn face recognition and 85.1% for single gallery toddler face recognition, forming state-of-the-results for both the databases. Comparison with several existing algorithms also showcases the effectiveness of the proposed algorithm on both the databases. © 2018 IEEE.

Face Recognition for Newborns, Toddlers, and Pre-School Children: A Deep Learning Approach

Digitally retouching images has become a popular trend, with people posting altered images on social media and even magazines posting flawless facial images of celebrities. Further, with advancements in Generative Adversarial Networks (GANs), now changing attributes and retouching have become very easy. Such synthetic alterations have adverse effect on face recognition algorithms. While researchers have proposed to detect image tampering, detecting GANs generated images has still not been explored. This paper proposes a supervised deep learning algorithm using Convolutional Neural Networks (CNNs) to detect synthetically altered images. The algorithm yields an accuracy of 99.65% on detecting retouching on the ND-IIITD dataset. It outperforms the previous state of the art which reported an accuracy of 87% on the database. For distinguishing between real images and images generated using GANs, the proposed algorithm yields an accuracy of 99.83%. © 2018 IEEE.

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018

On detecting GANs and retouching based synthetic alterations

Visual Object tracking research has undergone significant improvement in the past few years. The emergence of tracking by detection approach in tracking paradigm has been quite successful in many ways. Recently, deep convolutional neural networks have been extensively used in most successful trackers. Yet, the standard approach has been based on correlation or feature selection with minimal consideration given to motion consistency. Thus, there is still a need to capture various physical constraints through motion consistency which will improve accuracy, robustness and more importantly rotation adaptiveness. Therefore, one of the major aspects of this paper is to investigate the outcome of rotation adaptiveness in visual object tracking. Among other key contributions, the paper also includes various consistencies that turn out to be extremely effective in numerous challenging sequences than the current state-of-the-art. © 2018 IEEE.

Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018

Rotation Adaptive Visual Object Tracking with Motion Consistency

Accurate and robust visual object tracking is one of the most challenging computer vision problems. Recently, discriminative correlation filter trackers have shown promising results on benchmark datasets with continuous performance improvements in tracking accuracy and robustness. Still, these algorithms fail to track as the target object and background conditions undergo drastic changes over time. They are also incapable to resume tracking once the target is lost, limiting the ability to track long term. The proposed BoVW-CFT is a classifier-based generic technique to handle tracking uncertainties in correlation filter trackers. Tracking failures in correlation trackers are automatically identified and an image classifier with training, testing and online update stages is proposed as detector in the tracking scenario using Bag of Visual Words (BoVW) features. The proposed detector falls under the parts based model and is quite well suited in the tracking framework. Further, the online training stage in the proposed framework with updated model or training samples, incorporates temporal information, helping to detect rotated, blurred and scaled versions of the target. On detecting a target loss in the correlation tracker, the trained classifier, referred to as detector, is invoked to re-initialize the tracker with the actual target location. Therefore, for each tracking uncertainty, two output patches are obtained, one each from the base tracker and the classifier. The final target location is estimated using the normalized cross-correlation with the initial target patch. The method has the advantages of mitigating the model drift in correlation trackers and learns a robust model that tracks long term. Extensive experimental results demonstrate an improvement of 4.1% in the expected overlap, 1.86% in accuracy and 15.46% in robustness on VOT2016 and 1.82% in overlap precision, 2.32% in AUC and 2.87% in success rates on OTB100. © 2018 ACM.