Department of Computer Science & Engineering

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IIT Jodhpur

The Internet of Things (IoT) becomes a leading field of computer science, which simplifies the life of human beings. The IoT, devices can interconnect and communicate with each other, in a fully autonomous mode. This autonomy has made the IoT network vulnerable to various security threats. However, due to this autonomy, there is a strong need for reliable security and storage mechanism for authentication to exchange data between IoT devices. To solve this problem, we propose an efficient method known as SCAB-IoTA, which ensures identification and authentication of IoT devices and also provides secure communication in the open environment. Along with authentication and secure communication, the scheme also ensures data integrity. SCAB-IoTA used the blockchain and hybrid cryptosystem (the combination of Advanced Encryption Standard (AES) and Elliptic Curve Digital Signature Algorithm (ECDSA) cryptographic techniques) to enhance the security of IoT applications to prevent various attacks along with less computational, and storage overhead. Furthermore, we have a secure cluster of IoT devices based on angular distance (AD), so that devices can securely communicate without any interruption. A secure and robust trust mechanism has employed to build these clusters, where each IoT device has to authenticate itself before joining the cluster. The obtained results satisfy the IoT security requirements with 60% reduced computation, storage and communication cost compared with state-of-the-art methods. The security analysis illustrates that SCAB-IoTA withstands against various cyberattacks such as impersonation, message replay, man-in-the-middle, and botnet attacks. © 2021 Elsevier Inc.

World University root node

Journal of Parallel and Distributed Computing

SCAB - IoTA: Secure communication and authentication for IoT applications using blockchain

Automated vision-based score estimation models can be used to provide an alternate opinion to avoid judgment bias. Existing works have learned score estimation models by regressing the video representation to ground truth score provided by judges. However, such regression-based solutions lack interpretability in terms of giving reasons for the awarded score. One solution to make the scores more explicable is to compare the given action video with a reference video, which would capture the temporal variations vis-á-vis the reference video and map those variations to the final score. In this work, we propose a new action scoring system termed as Reference Guided Regression (RGR), which comprises (1) a Deep Metric Learning Module that learns similarity between any two action videos based on their ground truth scores given by the judges, and (2) a Score Estimation Module that uses the first module to find the resemblance of a video with a reference video to give the assessment score. The proposed scoring model is tested for Olympics Diving and Gymnastic vaults and the model outperforms the existing state-of-the-art scoring models. © 1991-2012 IEEE.

Fulltext

IEEE Transactions on Circuits and Systems for Video Technology

Action Quality Assessment Using Siamese Network-Based Deep Metric Learning

AAAI 2021

Few-Shot Lifelong Learning

Computer Vision and Image Understanding

Guest Editorial: Adversarial Deep Learning in Biometrics &amp; Forensics

The deep learning models, which include attention mechanisms, are shown to enhance the performance and efficiency of the various computer vision tasks such as pattern recognition, object detection, face recognition, etc. Although the visual attention mechanism is the source of inspiration for these models, recent attention models consider `attention' as a pure machine vision optimization problem, and visual attention remains the most neglected aspect. Therefore, this paper presents a collaborative human and machine attention module which considers both visual and network's attention. The proposed module is inspired by the dorsal ('where') pathways of visual processing and can be integrated with any convolutional neural network (CNN) model. First, the module computes the spatial attention map from the input feature maps, which is then combined with the visual attention maps. The visual attention maps are created using eye-fixations obtained by performing an eye-tracking experiment with human participants. The visual attention map covers the highly salient and discriminating image regions as humans tend to focus on such regions, whereas the other relevant image regions are processed by spatial attention map. The combination of these two maps results in the finer refinement in feature maps, resulting in improved performance. The comparative analysis reveals that our model not only shows significant improvement over the baseline model but also outperforms the other models. We hope that our findings using a collaborative human-machine attention module will be helpful in other computer vision tasks as well.

Proceedings - International Conference on Pattern Recognition

Collaborative Human Machine Attention Module for Character Recognition

In this paper we study a variant of the classic d -Hitting Set problem with lower and upper capacity constraints, say A and B, respectively. The input to the problem consists of a universe U, a set family, S, of sets over U, where each set in the family is of size at most d, a non-negative integer k; and additionally two functions α: S→ { 1, …, A} and β: S→ { 1, …, B}. The goal is to decide if there exists a hitting set of size at most k such that for every set S in the family S, the solution contains at least α(S) elements and at most β(S) elements from S. We call this the (A, B) -Multi d-Hitting Set problem. We study the problem in the realm of parameterized complexity. We show that (A, B) -Multi d-Hitting Set can be solved in O⋆(dk) time. For the special case when d= 3 and d= 4, we have an improved bound of O⋆(2. 2738 k) and O⋆(3. 562 k), respectively. The former matches the running time of the classical 3-Hitting Set problem. Furthermore, we show that if we do not have an upper bound constraint and the lower bound constraint is same for all the sets in the family, say A&amp;gt; 1, then the problem can be solved even faster than d-Hitting Set. We next investigate some graph-theoretic problems which can be thought of as an implicit d-Hitting Set problem. In particular, we study (A, B) -Multi Vertex Cover and (A, B) -Multi Feedback Vertex Set in Tournaments. In (A, B) -Multi Vertex Cover, we are given a graph G and a non-negative integer k, the goal is to find a subset S⊆ V(G) of size at most k such that for every edge in G, S contains at least A and at most B of its endpoints. Analogously, we can define (A, B) -Multi Feedback Vertex Set in Tournaments. We show that unlike Vertex Cover, which is same as (1, 2 ) -Multi Vertex Cover, (1, 1 ) -Multi Vertex Cover is polynomial-time solvable. Furthermore, unlike Feedback Vertex Set in Tournaments, (A, B) -Multi Feedback Vertex Set in Tournaments can be solved in polynomial time. © 2021, Springer Nature Switzerland AG.

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

Parameterized Complexity of d-Hitting Set with Quotas

The high susceptibility of deep learning algorithms against structured and unstructured perturbations has motivated the development of efficient adversarial defense algorithms. However, the lack of generalizability of existing defense algorithms and the high variability in the performance of the attack algorithms for different databases raises several questions on the effectiveness of the defense algorithms. In this research, we aim to design a defense model that is robust within a certain bound against both seen and unseen adversarial attacks. This bound is related to the visual appearance of an image, and we termed it as Visual Imperceptible Bound (VIB). To compute this bound, we propose a novel method that uses the database characteristics. The VIB is further used to measure the effectiveness of attack algorithms. The performance of the proposed defense model is evaluated on the MNIST, CIFAR-10, and Tiny ImageNet databases on multiple attacks that include C\&amp;W (l(2)) and DeepFool. The proposed defense model is not only able to increase the robustness against several attacks but also retain or improve the classification accuracy on an original clean test set. The proposed algorithm is attack agnostic, i.e. it does not require any knowledge of the attack algorithm.

Attack Agnostic Adversarial Defense via Visual Imperceptible Bound

In this paper, we propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework that combines facial age estimation and age-separated face verification. The key idea of this approach is to learn the age variations across time by conditioning the input image on the subject's gender and the target age group to which the face needs to be progressed. The loss function accounts for reducing the age gap between the original image and generated face image as well as preserving the identity. Both visual fidelity and quantitative evaluations demonstrate the efficacy of the proposed architecture on different facial age databases for age-separated face recognition.

Age Gap Reducer-GAN for Recognizing Age-Separated Faces

The vulnerability of deep networks towards adversarial perturbations has motivated the researchers to design detection and mitigation algorithms. Inspired by the dropout and dropconnect algorithms as well as augmentation techniques, this paper presents “PixelMask” based data augmentation as an efficient method of reducing the sensitivity of convolutional neural networks (CNNs) towards adversarial attacks. In the proposed approach, samples generated using PixelMask are used as augmented data, which helps in learning robust CNN models. Experiments performed with multiple databases and architectures show that the proposed PixelMask based data augmentation approach improves the classification performance on adversarially perturbed images. The proposed defense mechanism can be applied effectively for different adversarial attacks and can easily be combined with any deep neural network (DNN) architecture to increase the robustness. The effectiveness of the proposed defense is demonstrated in gray-box, white-box, and unseen train-test attack scenarios. For example, on the CIFAR-10 database under adaptive attack (i.e., projected gradient descent), the proposed PixelMask is able to improve the recognition performance of CNN by at-least 22.69%. Another advantage of the proposed algorithm over several existing defense algorithms is that the proposed defense either is able to retain or increase the classification accuracy of clean examples. © 2021 Elsevier B.V.

Pattern Recognition Letters

Cognitive data augmentation for adversarial defense via pixel masking

Abstract: The probability of the received bit values has been directly used in the performance enhanced reliability based direct decoding algorithm for turbo codes proposed recently and has achieved a significant coding gain improvement. This is in contrast to the maximum a posteriori (MAP)—type decoders and their variants in vogue which use log-likelihood ratio (LLR) in place of reliability and go through an iterative process. In this paper reliability has been identified in terms of probability densities of the bit values and the same used with the direct decoding algorithm. Extensive simulations with commonly used encoders show a clear performance improvement especially for short block lengths. Neither does the approach constrain noise distribution in any way as brought out by considering different distributions. © 2021, Pleiades Publishing, Inc.