Vision transformers (ViTs) and convolutional neural networks (CNNs) have demonstrated remarkable performance in classifying complicated hyperspectral images (HSIs). However, these models require a lot of computational power and training data. Recently, Kolmogorov–Arnold Networks (KANs) have been proposed as an effective network to overcome these challenges. In addition to learning new features, KANs may optimize learned features with outstanding accuracy due to their outward similarity to Multi-Layer Perceptrons (MLPs) and internal similarity to splines. Therefore, we introduce a novel gated-adaptable convolutional-based KAN (GC-KAN) for the HSI classification (HSIC). Our method combines a gated approach that selectively focuses on significant features and adaptable convolutional modules that adjust to intricate spectral-spatial changes. Extensive studies on the Houston 2013 and Indian Pines datasets demonstrate the efficacy of GC-KAN, demonstrating its improved performance over conventional techniques. This demonstrates GC-KAN’s potential as an effective tool for more thorough spatial-spectral feature extraction and accurate interpretation for several remote sensing applications.

Future frame prediction is a challenging task in computer vision with practical applications in areas such as video generation, autonomous driving, and robotics. Traditional recurrent neural networks have limited effectiveness in capturing long-range dependencies between frames, and combining convolutional neural networks (CNNs) with recurrent networks has limitations in modeling complex dependencies. Generative adversarial networks have shown promising results, but they are computationally expensive and suffer from instability during training. In this article, we propose a novel approach for future frame prediction that combines the encoding capabilities of 3-D CNNs with the sequence modeling capabilities of Transformers. We also propose a spatial self-attention mechanism and a novel neighborhood pixel intensity loss to preserve structural information and local intensity, respectively. Our approach outperforms existing methods in terms of structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and learned perceptual image patch similarity (LPIPS) scores on five public datasets. More precisely, our model exhibited an average improvement of 4.64%, 18.5%, and 42% concerning SSIM, PSNR, and LPIPS for the second most proficient method correspondingly, across all datasets. The results demonstrate the effectiveness of our proposed method in generating high-quality predictions of future frames.

An insider threat scenario refers to the outcome of a set of malicious activities caused by intentional or unintentional misuse of the organization's systems, networks, data, and resources. Prevention of insider threat is difficult, since trusted partners of the organization are involved in it, who have authorized access to these confidential/sensitive resources. The state-of-the-art research on insider threat detection mostly focuses on developing unsupervised behavioral anomaly detection techniques with the objective of finding out anomalousness or abnormal changes in user behavior over time. However, an anomalous activity is not necessarily malicious that can lead to an insider threat scenario. As an improvement to the existing approaches, we propose a technique for insider threat detection from time-series classification of user activities. Initially, a set of single-day features is computed from the user activity logs. A time-series feature vector is next constructed from the statistics of each single-day feature over a period of time. The label of each time-series feature vector (whether malicious or nonmalicious) is extracted from the ground truth. To classify the imbalanced ground-truth insider threat data consisting of only a small number of malicious instances, we employ a cost-sensitive data adjustment technique that undersamples the nonmalicious class instances randomly. As a classifier, we employ a two-layered deep autoencoder neural network and compare its performance with other popularly used classifiers: random forest and multilayer perceptron. Encouraging results are obtained by evaluating our approach using the CMU Insider Threat Data, which is the only publicly available insider threat data set consisting of about 14-GB web-browsing logs, along with logon, device connection, file transfer, and e-mail log files. We observe that both deep autoencoder and random forest classifiers classify the dataadjusted time-series feature set with high precision, recall, and f-score. Although multilayer perceptron has a high recall, it suffers from a lower precision and f-score compared to the other two classifiers.

As online automation, image processing and computer vision become increasingly powerful and sophisticated, methods to secure online assets from automated attacks (bots) are required. As traditional text based CAPTCHAs become more vulnerable to attacks, new methods for ensuring a user is human must be devised. To provide a solution to this problem, we aim to reduce some of the security shortcomings in an alternative style of CAPTCHA - more specifically, the image CAPTCHA. Introducing noise helps image CAPTCHAs thwart attacks from Reverse Image Search (RIS) engines and Computer Vision (CV) attacks while still retaining enough usability to allow humans to pass challenges. We present a secure image generation method based on noise addition that can be used for image CAPTCHAs, along with 4 different styles of image CAPTCHAs to demonstrate a fully functional image CAPTCHA challenge system.

Occlusion is one of the most challenging problems in many video processing applications such as surveillance, gait recognition, activity recognition and so on. Attempts have been made to develop algorithms for handling occlusion and evaluate their performance on various datasets. However, these studies are subjective in nature and the datasets are hardly characterised in terms of the level of occlusion, thereby precluding any form of quantitative comparison of performance. This shows a compelling need to design an explicit, unambiguous and quantitative model, which should be able to objectively represent occlusion in a video. This study proposes an occlusion model based on the position and pose uncertainties of the moving subjects in a video. The proposed occlusion model is able to characterise the level of occlusion present in a video. It is also employed to synthetically generate occlusion for walking sequences, thus providing a direction for controlled dataset generation against which human identification algorithms can be tested. Given an input video with a subject moving without any occlusion, a particle swarm optimisation‐based parameter estimation methodology is presented that generates the desired level of occlusion. The proposed approaches have been tested on the TUM‐IITKGP and PETS2010 datasets. Finally, as an application, the occlusion model has been used to generate an occluded gait datasets and the performances of different gait recognition algorithms have been compared under varying levels of occlusion.

In this study, the authors present a fully automated frontal (i.e. employing front and back views only) gait recognition approach using the depth information captured by multiple Kinect RGB‐D cameras. Limited depth sensing range restricts each of these Kinects to record only a part of a complete gait cycle of a walking subject. Hence, information from more than one Kinect is fused together to examine which features of a gait cycle can be conveniently extracted from the sequences captured independently by these cameras. To achieve this, it is imperative that the same subject be re‐identified as he moves from the field of view of one camera to another. The authors use a set of soft‐biometric features computed from the skeleton stream provided by Kinect software development kit) for doing automatic re‐identification. To enable such information fusion and also to handle missing components even after re‐identification, features are extracted at the granularity of small fractions of a gait cycle. Experiments carried out on a data set with gait videos captured by Kinects respectively from the back and front views show promising results.

Frontal gait recognition using partial cycle information has not received significant attention to date in spite of its many potential applications. In this paper, we propose a hierarchical classification strategy that combines front and back view features captured by RGB-D (Red Green Blue - Depth) cameras. Airport security check points are considered as a typical application scenario, where two depth cameras mounted on top of a metal detector gate positioned beyond a yellow line, respectively, record front and back views of a subject as he goes through the check-in process. Due to the short distance of the surveillance zone between the yellow line and point of exit, it is often not possible to capture a full gait cycle independently from the front view or back view. An initial stage of anthropometric feature-based classification followed by motion feature extraction from the front view is used to restrict the potential set of matched subjects. A final classification is then applied on this reduced set of subjects using depth features extracted from the back view. The method is computationally efficient with a much higher rate of accuracy compared with existing gait recognition approaches.