Gaurav Kumar Nayak

46 papers A* 4A 7B 1Journal 28Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	SIDiffAgent: Self-Improving Diffusion Agent. CoRR Shivank Garg, Ayush Singh, Gaurav Kumar Nayak
2025	J	`jnl`	DAD++: Improved data-free test time adversarial defense. Neurocomputing Gaurav Kumar Nayak, Inder Khatri, Shubham Randive, Ruchit Rawal, Anirban Chakraborty
2025	A	`conf`	DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-Id. WACV Nyle Siddiqui, Florinel-Alin Croitoru, Gaurav Kumar Nayak, Radu Tudor Ionescu, Mubarak Shah
2025	J	`jnl`	DeepSeaVision: Enhanced Detection and Classification of Underwater Species. IEEE Access G. Joshita Reddy, Keerthika Reddy, Sai Ruthvik Athota, Swathi Jamjala Narayanan, Boominathan Perumal, Gaurav Kumar Nayak
2025	J	`jnl`	MGD CoRR Jeffrey A. Chan-Santiago, Praveen Tirupattur, Gaurav Kumar Nayak, Gaowen Liu, Mubarak Shah
2025	A*	`conf`	MGD3 : Mode-Guided Dataset Distillation using Diffusion Models. ICML Jeffrey A. Chan-Santiago, Praveen Tirupattur, Gaurav Kumar Nayak, Gaowen Liu, Mubarak Shah
2025	J	`jnl`	Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning. CoRR M. Yashwanth, Gaurav Kumar Nayak, Harsh Rangwani, Arya Singh, R. Venkatesh Babu, Anirban Chakraborty
2025	—	`conf`	Open-DeBias: Toward Mitigating Open-Set Bias in Language Models. EMNLP (Findings) Arti Rani, Shweta Singh, Nihar Ranjan Sahoo, Gaurav Kumar Nayak
2025	J	`jnl`	Open-DeBias: Toward Mitigating Open-Set Bias in Language Models. CoRR Arti Rani, Shweta Singh, Nihar Ranjan Sahoo, Gaurav Kumar Nayak
2025	J	`jnl`	Robust Few-Shot Learning Without Using Any Adversarial Samples. IEEE Trans. Neural Networks Learn. Syst. Gaurav Kumar Nayak, Ruchit Rawal, Inder Khatri, Anirban Chakraborty
2024	J	`jnl`	Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning. Trans. Mach. Learn. Res. M. Yashwanth, Gaurav Kumar Nayak, Arya Singh, Yogesh Simmhan, Anirban Chakraborty
2024	—	`conf`	CityGuessr: City-Level Video Geo-Localization on a Global Scale. ECCV (63) Parth Parag Kulkarni, Gaurav Kumar Nayak, Mubarak Shah
2024	J	`jnl`	CityGuessr: City-Level Video Geo-Localization on a Global Scale. CoRR Parth Parag Kulkarni, Gaurav Kumar Nayak, Mubarak Shah
2024	J	`jnl`	DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-ID. CoRR Nyle Siddiqui, Florinel-Alin Croitoru, Gaurav Kumar Nayak, Radu Tudor Ionescu, Mubarak Shah
2024	—	`conf`	Data-free Defense of Black Box Models Against Adversarial Attacks. CVPR Workshops Gaurav Kumar Nayak, Inder Khatri, Ruchit Rawal, Anirban Chakraborty
2024	A	`conf`	Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning. WACV M. Yashwanth, Gaurav Kumar Nayak, Harsh Rangwani, Arya Singh, R. Venkatesh Babu, Anirban Chakraborty
2023	J	`jnl`	DAD++: Improved Data-free Test Time Adversarial Defense. CoRR Gaurav Kumar Nayak, Inder Khatri, Shubham Randive, Ruchit Rawal, Anirban Chakraborty
2023	A	`conf`	DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers. WACV Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty
2023	—	`conf`	DISBELIEVE: Distance Between Client Models Is Very Essential for Effective Local Model Poisoning Attacks. ISIC/Care-AI/MedAGI/DeCaF@MICCAI Indu Joshi, Priyank Upadhya, Gaurav Kumar Nayak, Peter J. Schüffler, Nassir Navab
2023	J	`jnl`	DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks. CoRR Indu Joshi, Priyank Upadhya, Gaurav Kumar Nayak, Peter J. Schüffler, Nassir Navab
2023	J	`jnl`	Federated Learning on Heterogeneous Data via Adaptive Self-Distillation. CoRR M. Yashwanth, Gaurav Kumar Nayak, Arya Singh, Yogesh Singh, Anirban Chakraborty
2023	A*	`conf`	GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization. NeurIPS Vicente Vivanco Cepeda, Gaurav Kumar Nayak, Mubarak Shah
2023	J	`jnl`	GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization. CoRR Vicente Vivanco Cepeda, Gaurav Kumar Nayak, Mubarak Shah
2022	A	`conf`	DAD: Data-free Adversarial Defense at Test Time. WACV Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty
2022	J	`jnl`	DAD: Data-free Adversarial Defense at Test Time. CoRR Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty
2022	J	`jnl`	DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers. CoRR Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty
2022	J	`jnl`	Data-free Defense of Black Box Models Against Adversarial Attacks. CoRR Gaurav Kumar Nayak, Inder Khatri, Shubham Randive, Ruchit Rawal, Anirban Chakraborty
2022	—	`conf`	Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems. CVPR Workshops Gaurav Kumar Nayak, Ruchit Rawal, Rohit Lal, Himanshu Patil, Anirban Chakraborty
2022	J	`jnl`	Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems. CoRR Gaurav Kumar Nayak, Ruchit Rawal, Rohit Lal, Himanshu Patil, Anirban Chakraborty
2022	J	`jnl`	Mining Data Impressions From Deep Models as Substitute for the Unavailable Training Data. IEEE Trans. Pattern Anal. Mach. Intell. Gaurav Kumar Nayak, Konda Reddy Mopuri, Saksham Jain, Anirban Chakraborty
2022	J	`jnl`	Query Efficient Cross-Dataset Transferable Black-Box Attack on Action Recognition. CoRR Rohit Gupta, Naveed Akhtar, Gaurav Kumar Nayak, Ajmal Mian, Mubarak Shah
2022	J	`jnl`	Robust Few-shot Learning Without Using any Adversarial Samples. CoRR Gaurav Kumar Nayak, Ruchit Rawal, Inder Khatri, Anirban Chakraborty
2021	A	`conf`	Beyond Classification: Knowledge Distillation using Multi-Object Impressions. BMVC Gaurav Kumar Nayak, Monish Keswani, Sharan Seshadri, Anirban Chakraborty
2021	J	`jnl`	Beyond Classification: Knowledge Distillation using Multi-Object Impressions. CoRR Gaurav Kumar Nayak, Monish Keswani, Sharan Seshadri, Anirban Chakraborty
2021	J	`jnl`	Data Impressions: Mining Deep Models to Extract Samples for Data-free Applications. CoRR Gaurav Kumar Nayak, Konda Reddy Mopuri, Saksham Jain, Anirban Chakraborty
2021	A	`conf`	Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation. WACV Gaurav Kumar Nayak, Konda Reddy Mopuri, Anirban Chakraborty
2021	A	`conf`	Incremental Learning for Animal Pose Estimation using RBF k-DPP. BMVC Gaurav Kumar Nayak, Het Shah, Anirban Chakraborty
2021	J	`jnl`	Incremental Learning for Animal Pose Estimation using RBF k-DPP. CoRR Gaurav Kumar Nayak, Het Shah, Anirban Chakraborty
2020	A*	`conf`	DeGAN: Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier. AAAI Sravanti Addepalli, Gaurav Kumar Nayak, Anirban Chakraborty, Venkatesh Babu Radhakrishnan
2020	J	`jnl`	Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation. CoRR Gaurav Kumar Nayak, Konda Reddy Mopuri, Anirban Chakraborty
2020	—	`conf`	Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images. BigMM Gaurav Kumar Nayak, Saksham Jain, R. Venkatesh Babu, Anirban Chakraborty
2020	J	`jnl`	Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images. CoRR Gaurav Kumar Nayak, Saksham Jain, R. Venkatesh Babu, Anirban Chakraborty
2019	J	`jnl`	DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier. CoRR Sravanti Addepalli, Gaurav Kumar Nayak, Anirban Chakraborty, R. Venkatesh Babu
2019	B	`conf`	Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP). ICIP Gaurav Kumar Nayak, Utkarsh Shreemali, R. Venkatesh Babu, Anirban Chakraborty
2019	A*	`conf`	Zero-Shot Knowledge Distillation in Deep Networks. ICML Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, Anirban Chakraborty
2019	J	`jnl`	Zero-Shot Knowledge Distillation in Deep Networks. CoRR Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, R. Venkatesh Babu, Anirban Chakraborty

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.