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Anubhav Jain
Researcher at Indraprastha Institute of Information Technology
Publications - 5
Citations - 68
Anubhav Jain is an academic researcher from Indraprastha Institute of Information Technology. The author has contributed to research in topics: Public transport & Benchmark (computing). The author has an hindex of 3, co-authored 5 publications receiving 42 citations.
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Proceedings ArticleDOI
On Detecting GANs and Retouching based Synthetic Alterations
TL;DR: A supervised deep learning algorithm using Convolutional Neural Networks (CNNs) to detect synthetically altered images and yields an accuracy of 99.65% on detecting retouching on the ND-IIITD dataset, which outperforms the previous state of the art.
Proceedings ArticleDOI
Detecting GANs and Retouching Based Digital Alterations via DAD-HCNN
TL;DR: A hierarchical approach termed as DAD-HCNN which performs two-fold task: it differentiates between digitally generated images and digitally retouched images from the original unaltered images, and to increase the explainability of the decision, it also identifies the GAN architecture used to create the image.
Posted Content
On Detecting GANs and Retouching based Synthetic Alterations.
TL;DR: In this article, a supervised deep learning algorithm using Convolutional Neural Networks (CNNs) was proposed to detect synthetically altered images and achieved an accuracy of 99.65% on detecting retouching on the ND-IIITD dataset.
Proceedings ArticleDOI
Benchmark Dataset for Timetable optimization of Bus Routes in the City of New Delhi
TL;DR: In this article, the authors presented a real-time GPS bus transit data for over 500 routes of buses operating in New Delhi and presented an approach to reduce the waiting time of Delhi buses by analyzing the traffic behavior and proposing a timetable.
Posted Content
Benchmark Dataset for Timetable Optimization of Bus Routes in the City of New Delhi
TL;DR: This research presents a novel realtime GPS bus transit data for over 500 routes of buses operating in New Delhi that can be used for modeling various timetable optimization tasks as well as in other domains such as traffic management and travel time estimation.