Atul Mishra

L. D. M. S. Sai Teja, N. Siva Gopala Krishna, Ufaq Khan et al.

In the age of advanced large language models (LLMs), the boundaries between human and AI-generated text are becoming increasingly blurred. We address the challenge of segmenting mixed-authorship text, that is identifying transition points in text where authorship shifts from human to AI or vice-versa, a problem with critical implications for authenticity, trust, and human oversight. We introduce a novel framework, called Info-Mask for mixed authorship detection that integrates stylometric cues, perplexity-driven signals, and structured boundary modeling to accurately segment collaborative human-AI content. To evaluate the robustness of our system against adversarial perturbations, we construct and release an adversarial benchmark dataset Mixed-text Adversarial setting for Segmentation (MAS), designed to probe the limits of existing detectors. Beyond segmentation accuracy, we introduce Human-Interpretable Attribution (HIA overlays that highlight how stylometric features inform boundary predictions, and we conduct a small-scale human study assessing their usefulness. Across multiple architectures, Info-Mask significantly improves span-level robustness under adversarial conditions, establishing new baselines while revealing remaining challenges. Our findings highlight both the promise and limitations of adversarially robust, interpretable mixed-authorship detection, with implications for trust and oversight in human-AI co-authorship.

Deepika Dua, Atul Mishra

Mobile ad-hoc networks(MANET) is the collection of mobile nodes which are self organizing and are connected by wireless links where nodes which are not in the direct range communicate with each other relying on the intermediate nodes. As a result of trusting other nodes in the route, a malicious node can easily compromise the security of the network. A black-hole node is the malicious node which drops the entire packet coming to it and always shows the fresh route to the destination, even if the route to destination doesn't exist. This paper describes a scheme that will detect the intrusion in the network in the presence of black-hole node and its performance is compared with the previous technique. This novel technique helps to increase the network performance by reducing the overhead in the network.

Chetan Kaushik, Atul Mishra

Rapid increase in the volume of sentiment rich social media on the web has resulted in an increased interest among researchers regarding Sentimental Analysis and opinion mining. However, with so much social media available on the web, sentiment analysis is now considered as a big data task. Hence the conventional sentiment analysis approaches fails to efficiently handle the vast amount of sentiment data available now a days. The main focus of the research was to find such a technique that can efficiently perform sentiment analysis on big data sets. A technique that can categorize the text as positive, negative and neutral in a fast and accurate manner. In the research, sentiment analysis was performed on a large data set of tweets using Hadoop and the performance of the technique was measured in form of speed and accuracy. The experimental results shows that the technique exhibits very good efficiency in handling big sentiment data sets.