Pamela Johnston

15 papers B 3C 4Journal 6Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	On the Impact of Greyscale ImageNet Pre-training for Chest X-Ray Model Transferability. IDEAL (1) Sophie Crawford Haynes, Pamela Johnston, Eyad Elyan
2024	J	`jnl`	Generalisation challenges in deep learning models for medical imagery: insights from external validation of COVID-19 classifiers. Multim. Tools Appl. Sophie Crawford Haynes, Pamela Johnston, Eyad Elyan
2023	C	`conf`	Student Interaction with a Virtual Learning Environment: An Empirical Study of Online Engagement Behaviours During and Since the Time of COVID-19. FIE Pamela Johnston, Mark Zarb, Carlos Francisco Moreno-García
2023	B	`conf`	Unmasking the Imposters: Task-specific feature learning for face presentation attack detection. IJCNN Faseela Abdullakutty, Eyad Elyan, Pamela Johnston
2022	J	`jnl`	Deep Transfer Learning on the Aggregated Dataset for Face Presentation Attack Detection. Cogn. Comput. Faseela Abdullakutty, Eyad Elyan, Pamela Johnston, Adamu Ali-Gombe
2022	J	`jnl`	Fusion Methods for Face Presentation Attack Detection. Sensors Faseela Abdullakutty, Pamela Johnston, Eyad Elyan
2021	J	`jnl`	A review of state-of-the-art in Face Presentation Attack Detection: From early development to advanced deep learning and multi-modal fusion methods. Inf. Fusion Faseela Abdullakutty, Eyad Elyan, Pamela Johnston
2021	C	`conf`	Face Spoof Detection: An Experimental Framework. EANN Faseela Abdullakutty, Eyad Elyan, Pamela Johnston
2020	B	`conf`	Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks. IJCNN Carlos Francisco Moreno-García, Pamela Johnston, Bello Garkuwa
2020	C	`conf`	Symbols in Engineering Drawings (SiED): An Imbalanced Dataset Benchmarked by Convolutional Neural Networks. EANN Eyad Elyan, Carlos Francisco Moreno-García, Pamela Johnston
2020	J	`jnl`	Video tampering localisation using features learned from authentic content. Neural Comput. Appl. Pamela Johnston, Eyad Elyan, Chrisina Jayne
2019	J	`jnl`	A review of digital video tampering: From simple editing to full synthesis. Digit. Investig. Pamela Johnston, Eyad Elyan
2019	—	`—`	Beyond the pixels: learning and utilising video compression features for localisation of digital tampering. Pamela Johnston
2018	B	`conf`	Spatial Effects of Video Compression on Classification in Convolutional Neural Networks. IJCNN Pamela Johnston, Eyad Elyan, Chrisina Jayne
2018	C	`conf`	Toward Video Tampering Exposure: Inferring Compression Parameters from Pixels. EANN Pamela Johnston, Eyad Elyan, Chrisina Jayne

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.

In 2025, he co-authored Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, published at CheckMATE. The paper evaluates the effectiveness of obfuscation techniques and their impact on source-level static analysis and binary-level decompilation.

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.