Department of Computer Science & Engineering

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IIT Jodhpur

Hardware-based security primitives play important roles in protecting and securing a system in Internet of Things (IoT) applications. The main primitives are physical unclonable functions (PUF) and true random number generator (TRNG) studied in this paper. Efficient FPGA implementation are proposed in the work along with relevant security analysis using prevalent metrics. Finally, an application of designed TRNG and PUF is proposed for implementing an authenticated key agreement protocol, © 2020 IEEE.

World University root node

IEEE/IFIP International Conference on VLSI and System-on-Chip, VLSI-SoC

Design, Implementation and Analysis of Efficient Hardware-Based Security Primitives

Inspired by the works of Nyberg and Knudsen, the wide trail strategy suggests to ensure that the number of active S-boxes in a differential characteristic or a linear approximation is sufficiently high, thus, offering security against differential and linear attacks. Many cipher designers are relying on this strategy, and most new designs include analysis based on counting the number of active S-boxes. Unfortunately, this analysis is not always accurate and needs to be performed in a very delicate manner. To counter the common approach, we give an example of a 4-round Feistel construction with a very large number of active S-boxes that is expected to resist differential and linear cryptanalysis. However, we show that S-box counting arguments are insufficient in cases where one can find many differential characteristics with the same input and output difference. Namely, we show for a “provably” secure 128-bit block, 4-round Feistel with at least 36 active AES S-boxes, that one can construct differential characteristics with probability 2- 118 much higher than the bound of 2- 216. Even if we compare this 4-round Feistel construction to a random permutation we obtain a 10x factor in the probability of the characteristic. © Springer Nature Switzerland AG 2020.

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

Counting active S-boxes is not enough

Given a graph (formula presented), a subset (formula presented) is said to be a feedback vertex set of G if (formula presented) is a forest. In the Feedback Vertex Set (FVS) problem, we are given an undirected graph G, and a positive integer k, the question is whether there exists a feedback vertex set of size at most k. This problem is extremely well studied in the realm of parameterized complexity. In this paper, we study three variants of the FVS problem: Unrestricted Fair FVS, Restricted Fair FVS, and Relax Fair FVS. In Unrestricted Fair FVS problem, we are given a graph G and a positive integer (formula presented), the question is does there exists a feedback vertex set (formula presented) (of any size) such that for every vertex (formula presented), v has at most (formula presented) neighbours in S. First, we study Unrestricted Fair FVS from different parameterizations such as treewidth, treedepth and neighbourhood diversity and obtain several results (both tractability and intractability). Next, we study Restricted Fair FVS problem, where we are also given an integer k in the input and we demand the size of S to be at most k. This problem is trivially NP-complete; we show that Restricted Fair FVS problem when parameterized by the solution size k and the maximum degree (formula presented) of the graph G, admits a kernel of size (formula presented). Finally, we study Relax Fair FVS problem, where we want that the size of S is at most k and for every vertex outside S, that is, for all (formula presented), v has at most (formula presented) neighbours in S. We give an FPT algorithm for Relax Fair FVS problem running in time (formula presented), for a fixed constant c. © Springer Nature Switzerland AG 2020.

Parameterized complexity of fair feedback vertex set problem

We propose a fast and accurate solution to speckle reduction targeted specifically at optical coherence tomography images. The proposed speckle removing filter is designed using a novel potential function based on the gradient of the local variance of intensity. After filtering, the spatially neighboring pixels with close values of intensities converge to uniform gray values, while the edges remain intact. This filtering process results in removal of speckle without destroying the edges of the desired object. The proposed filter also prevents the generation of any false edges. Detailed experimental analysis shows at least 1-dB improvement in the peak signal-to-noise ratio for spectral domain optical coherence tomography images. The method also shows superior edge preservation, contrast, and speed compared to the state of the art in speckle removing filters. © 2013 IEEE.

IEEE Journal of Biomedical and Health Informatics

Speckle Removal Using Diffusion Potential for Optical Coherence Tomography Images

We propose a reinforced quasi-random forest for classification task. Reinforcement is performed iteratively by adding new trees to the forest. Our method assigns an importance to each of the attributes and identifies the attributes that causes the mis-classification of data points during training. The new trees are constructed using the mis-classified data points with reduced set of attributes. The attributes for splitting the nodes of the reinforced trees are found in a deterministic manner. Hence the new trees are quasi-random in nature. The best out of all the new trees are found using a novel electrostatic model. These trees are termed as reinforced trees. Additions of reinforced trees to the existing forest ensure maximum reduction in classification error. The efficacy of the proposed method is established through experiments on breast cancer datasets for detecting mitotic nuclei. Results of our method show significant improvement compared to other state-of-the-art approaches. Results on benchmark datasets show as much as 14% reduction in classification error. © 2019

Pattern Recognition

Reinforced quasi-random forest

Automatic image annotation aims at assigning a set of textual labels to an image that describes its semantics. In real-world datasets with large vocabularies, each image is usually annotated with only a subset of all possible relevant labels. This leads to the problem of learning with missing labels. Moreover, several existing approaches for image annotation aim at producing a set which contains as many relevant labels as possible for a given image. However, such a set is usually unnecessary and contains redundant labels. This leads to the task of diverse image annotation, which aims at predicting labels that are collectively representative as well as diverse. In this paper, we study a new task called diverse image annotation with missing labels (DIAML), which is a fusion of these two practical aspects of the conventional image annotation task; i.e., diverse image annotation, and image annotation with missing labels. For this, we also propose a new k-nearest neighbours (kNN)based algorithm, called per-label k-nearest neighbours (or PL-kNN), that addresses both these challenges simultaneously. For a given image, rather than identifying its neighbours in the feature-space as done in the conventional kNN algorithm, PL-kNN first creates diverse partitions of the (training)samples based on label information, and then predicts the confidence for each label using a fixed number of nearest neighbours from the corresponding partition. The label-specific partitioning and neighbourhood selection steps inherently address the issue of missing labels as well. Extensive experiments on benchmark datasets show that though conceptually simple, the proposed method consistently outperforms state-of-the-art methods that address either of these two (sub-)tasks, thus establishing a strong baseline for the new DIAML task. © 2019 Elsevier Ltd

Diverse image annotation with missing labels

This paper reviews the first NTIRE challenge on video super-resolution (restoration of rich details in low-resolution video frames) with focus on proposed solutions and results. A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks. Track 1 employed standard bicubic downscaling setup while Track 2 had realistic dynamic motion blurs. Each competition had 124 and 104 registered participants. There were total 14 teams in the final testing phase. They gauge the state-of-the-art in video super-resolution. © 2019 IEEE.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

NTIRE 2019 challenge on video super-resolution: Methods and results

Automated primate face recognition has enormous potential in effective conservation of species facing endangerment or extinction. The task is characterized by lack of training data, low inter-class variations, and large intra-class differences. Owing to the challenging nature of the problem, limited research has been performed to automate the process of primate face recognition. In this research, we propose a novel Triplet Transform Learning (TTL) model for learning discriminative representations of primate faces. The proposed model reduces the intra-class variations and increases the inter-class variations to obtain robust sparse representations for the primate faces. It is utilized to present a novel framework for primate face recognition, which is evaluated on the primate dataset, comprising of 80 identities including monkeys, gorillas, and chimpanzees. Experimental results demonstrate the efficacy of the proposed approach, where it outperforms the existing approaches and attains state-of-the-art performance on the primates database. © 2019 IEEE.

Proceedings - International Conference on Image Processing, ICIP

Triplet Transform Learning for Automated Primate Face Recognition

Deaths and injuries are common in road accidents, violence, and natural disaster. In such cases, one of the main tasks of responders is to retrieve the identity of the victims to reunite families and ensure proper identification of deceased/injured individuals. Apart from this, identification of unidentified dead bodies due to violence and accidents is crucial for the police investigation. In the absence of identification cards, current practices for this task include DNA profiling and dental profiling. Face is one of the most commonly used and widely accepted biometric modalities for recognition. However, face recognition is challenging in the presence of facial injuries such as swelling, bruises, blood clots, laceration, and avulsion which affect the features used in recognition. In this paper, for the first time, we address the problem of injured face recognition and propose a novel Subclass Contrastive Loss (SCL) for this task. A novel database, termed as Injured Face (IF) database, is also created to instigate research in this direction. Experimental analysis shows that the proposed loss function surpasses existing algorithm for injured face recognition. © 2019 IEEE.

2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems, BTAS 2019

Subclass Contrastive Loss for Injured Face Recognition

Several computer vision applications such as object detection and face recognition have started to completely rely on deep learning based architectures. These architectures, when paired with appropriate loss functions and optimizers, produce state-of-the-art results in a myriad of problems. On the other hand, with the advent of 'blockchain', the cybersecurity industry has developed a new sense of trust which was earlier missing from both the technical and commercial perspectives. Employment of cryptographic hash as well as symmetric/asymmetric encryption and decryption algorithms ensure security without any human intervention (i.e., centralized authority). In this research, we present the synergy between the best of both these worlds. We first propose a model which uses the learned parameters of a typical deep neural network and is secured from external adversaries by cryptography and blockchain technology. As the second contribution of the proposed research, a new parameter tampering attack is proposed to properly justify the role of blockchain in machine learning. © 2019 IEEE.