Faouzi Adjed - RankMe

17 papers A* 1C 2Journal 7Unranked 7

Year	Rank	Type	Title / Venue / Authors
2024	C	`conf`	AI Systems Trustworthiness Assessment: State of the Art. MODELSWARD Afef Awadid, Kahina Amokrane-Ferka, Henri Sohier, Juliette Mattioli, Faouzi Adjed, Martin Gonzalez, Souhaiel Khalfaoui
2024	A*	`conf`	Confidence Calibration of Classifiers with Many Classes. NeurIPS Adrien Le-Coz, Stéphane Herbin, Faouzi Adjed
2024	J	`jnl`	Confidence Calibration of Classifiers with Many Classes. CoRR Adrien Le-Coz, Stéphane Herbin, Faouzi Adjed
2024	—	`conf`	Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models. CVPR Workshops Adrien Le-Coz, Houssem Ouertatani, Stéphane Herbin, Faouzi Adjed
2024	J	`jnl`	Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models. CoRR Adrien Le-Coz, Houssem Ouertatani, Stéphane Herbin, Faouzi Adjed
2024	J	`jnl`	Explaining an image classifier with a generative model conditioned by uncertainty. CoRR Adrien Le-Coz, Stéphane Herbin, Faouzi Adjed
2024	J	`jnl`	Leveraging generative models to characterize the failure conditions of image classifiers. CoRR Adrien Le-Coz, Stéphane Herbin, Faouzi Adjed
2023	—	`conf`	Explaining an Image Classifier with a Generative Model Conditioned by Uncertainty. PKDD/ECML Workshops (2) Adrien Le-Coz, Stéphane Herbin, Faouzi Adjed
2022	J	`jnl`	Coupling algebraic topology theory, formal methods and safety requirements toward a new coverage metric for artificial intelligence models. Neural Comput. Appl. Faouzi Adjed, Mallek Mziou-Sallami, Frédéric Pelliccia, Mehdi Rezzoug, Lucas Schott, Christophe Bohn, Yesmina Jaâfra
2022	—	`conf`	Integrative System of Deep Classifiers Certification: Case of Convolutional Attacks. ICAART (Revised Selected Paper Imen Smati, Rania Khalsi, Mallek Mziou-Sallami, Faouzi Adjed, Faouzi Ghorbel
2022	—	`conf`	Leveraging generative models to characterize the failure conditions of image classifiers. AISafety@IJCAI Adrien Le-Coz, Stéphane Herbin, Faouzi Adjed
2022	—	`conf`	Towards a Certification of Deep Image Classifiers against Convolutional Attacks. ICAART (2) Mallek Mziou-Sallami, Faouzi Adjed
2021	—	`conf`	Deep Learning Architecture for Topological Optimized Mechanical Design Generation with Complex Shape Criterion. IEA/AIE (1) Waad Almasri, Dimitri Bettebghor, Fakhreddine Ababsa, Florence Danglade, Faouzi Adjed
2021	C	`conf`	GMCAD: an original Synthetic Dataset of 2D Designs along their Geometrical and Mechanical Conditions. ISM Waad Almasri, Dimitri Bettebghor, Faouzi Adjed, Fakhreddine Ababsa, Florence Danglade
2018	J	`jnl`	Fusion of structural and textural features for melanoma recognition. IET Comput. Vis. Faouzi Adjed, Syed Jamal Safdar Gardezi, Fakhreddine Ababsa, Ibrahima Faye, Sarat Chandra Dass
2018	J	`jnl`	Segmentation of pectoral muscle using the adaptive gamma corrections. Multim. Tools Appl. Syed Jamal Safdar Gardezi, Faouzi Adjed, Ibrahima Faye, Nidal S. Kamel, Mohamed Meselhy Eltoukhy
2016	—	`conf`	Mammogram Classification Using Curvelet GLCM Texture Features and GIST Features. AISI Syed Jamal Safdar Gardezi, Ibrahima Faye, Faouzi Adjed, Nidal S. Kamel, Mohamed Meselhy Eltoukhy

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.