Anindita Ghosh
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Anindita Ghosh has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Bing Zhou
Army Engineering University, Shijiazhuang 050000, China
2 shared publications - Christian Theobalt
Max Planck Institute for Informatics, Saarland Informatics Campus
2 shared publications - Chuan Guo
Snap Inc., New York, USA
2 shared publications - Jian Wang
Snap Inc
2 shared publications - Philipp Slusallek
Saarland University, Saarbrücken, Germany
2 shared publications - Rishabh Dabral
Max Planck Institute for Informatics, Saarbrücken, Germany
2 shared publications
Latest Publications
- Top Cited Papers
- 6 February 2026
- Published by Springer Nature in International Journal of Computer Vision
- Vol. 134 (3) , 113
A Survey on Human Interaction Motion Generation
- 6 February 2026
- Published by Springer Nature in International Journal of Computer Vision
- Vol. 134 (3) , 113
Top Cited PapersA Survey on Human Interaction Motion Generation
- Top Cited PapersOpen Access
- 27 July 2025
- Published by Association for Computing Machinery (ACM)
- p. 1-11
DuetGen: Music Driven Two-Person Dance Generation via Hierarchical Masked Modeling
- 27 July 2025
- Published by Association for Computing Machinery (ACM)
- p. 1-11
Top Cited PapersOpen AccessDuetGen: Music Driven Two-Person Dance Generation via Hierarchical Masked Modeling
- 25 October 2024
- Published by Springer Nature
- p. 418-437
REMOS: 3D Motion-Conditioned Reaction Synthesis for Two-Person Interactions
- 25 October 2024
- Published by Springer Nature
- p. 418-437
REMOS: 3D Motion-Conditioned Reaction Synthesis for Two-Person Interactions
- 8 December 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
Adversarial Machine Learning Challenges in Modern Network Security Systems
In the rapidly evolving landscape of network security, the threat of adversarial attacks on machine learning models has emerged as a critical concern. In response, we propose the Adversarial Robust Network Security Framework (ARNetSec), a comprehensive defense strategy integrating three pivotal algorithms—Adversarial Training (AT), Randomized Smoothing (RS), and Defensive Distillation (DD). AT involves training the model on a mixture of clean data and adversarial examples, generated through the Fast Gradient Sign Method (FGSM). RS adds random noise to input data during inference, creating uncertainty that hampers adversarial attempts. DD employs temperature scaling in the softmax function, smoothing the probability distribution and enhancing robustness. This framework collectively fortifies machine learning models, bolstering their resistance against adversarial attacks, and substantially improving their generalization capabilities.
- 8 December 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-6
Adversarial Machine Learning Challenges in Modern Network Security Systems
In the rapidly evolving landscape of network security, the threat of adversarial attacks on machine learning models has emerged as a critical concern. In response, we propose the Adversarial Robust Network Security Framework (ARNetSec), a comprehensive defense strategy integrating three pivotal algorithms—Adversarial Training (AT), Randomized Smoothing (RS), and Defensive Distillation (DD). AT involves training the model on a mixture of clean data and adversarial examples, generated through the Fast Gradient Sign Method (FGSM). RS adds random noise to input data during inference, creating uncertainty that hampers adversarial attempts. DD employs temperature scaling in the softmax function, smoothing the probability distribution and enhancing robustness. This framework collectively fortifies machine learning models, bolstering their resistance against adversarial attacks, and substantially improving their generalization capabilities.