Here's a list of thesis proposed by the members of the lab. These are basically ideas from which develop a more structured work once the topic of interest is selected. Basic knowledge of deep learning and computer vision is required to engage with these topics effectively. 

Understanding Representations in Optimized Vision Transformers

Keywords: Information theory, Entropy estimation, Efficient architectures

Abstract: This thesis investigates how optimizing different parts of transformer-based models affects the quality of learned representations. Focusing on encoder or decoder embeddings in monocular depth estimation, the study compares encoder, decoder, and full-model optimization. Insights can be extended to different computer vision tasks.

Supervisors: Irene Amerini (amerini@diag.uniroma1.it), Claudio Schiavella (schiavella@diag.uniroma1.it), Lorenzo Cirillo (cirilo@diag.uniroma1.it)  

References:

1. Kingma et al., Auto-Encoding Variational Bayes, 2013.

2. Tishby et al., Deep learning and the information bottleneck principle. 2015.

Learning to Detect Deepfakes in the Presence of Social Network Image Degradation

Keywords: Deepfake detection, Social network compression, Forensic traces

Abstract: This thesis investigates deepfake detection under real-world social network compression, where platform-specific encoding and block artifacts significantly distort forensic traces. We will explore both model-side robustness strategies (e.g., artifact-suppression and attention guidance) and data-side emulation techniques to reproduce realistic compression pipelines, aiming to improve generalization from lab conditions to in-the-wild content. 

Supervisors: Irene Amerini (amerini@diag.uniroma1.it), Simone Teglia (teglia@diag.uniroma1.it)

References:

1. Montibeller, A., Shullani, D., Baracchi, D., Piva, A., & Boato, G. (2025, October). Bridging the Gap: A Framework for Real-World Video Deepfake Detection via Social Network Compression Emulation. Proceedings of the 1st on Deepfake Forensics Workshop: Detection, Attribution, Recognition, and Adversarial Challenges in the Era of AI-Generated Media, 29–36. doi:10.1145/3746265.3759670

2. Li, M., Tao, R., Liu, Y., Tan, C., Qin, H., Li, B., … Zhao, Y. (2025). Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks. arXiv [Cs.CV]. Retrieved from http://arxiv.org/abs/2506.20548

Enhancing and Explaining Deep Learning Models for Periodontal Disease Assessment using GANs and XAI

Keywords: Generative Adversarial Networks, Explainable AI, Medical Imaging, Dental Radiography

Abstract: This thesis aims to advance deep learning for periodontal analysis by tackling data scarcity and model opacity. We will develop a generative framework to augment the training dataset, improving model robustness and accuracy. Concurrently, we will integrate explainable AI methods to support the models' diagnostic reasoning, ensuring their outputs are transparent, clinically relevant and trustworthy.

Supervisors: Irene Amerini (amerini@diag.uniroma1.it), Gianmarco Scarano (gianmarco.scarano@uniroma1.it), Lorenzo Cirillo (cirilo@diag.uniroma1.it)

References:

1. T K, Lakshmi and Kshirsagar, Pravin R. and Akula, Vijaya Krishna and Kuan Tak, Tan and Kumar, Kailash and Mangalapu, Naresh Kumar, Severity Assessment of Periodontal Disease from Oral Images with GAN-Augmented Data and Ensembled Deep Learning. Available at SSRN: https://ssrn.com/abstract=5497365 or http://dx.doi.org/10.2139/ssrn.5497365

2. Glick, A., Clayton, M., Angelov, N., & Chang, J. (2022). Impact of explainable artificial intelligence assistance on clinical decision-making of novice dental clinicians. JAMIA Open, 5(2). Retrieved from https://www.scopus.com/pages/publications/85134949460

Architectural vs General Optimizations in Vision Transformers

Keywords: Attention optimization, Efficient architectures, Model compression 

Abstract: This thesis compares two key approaches to optimizing Vision Transformers: architectural changes (e.g., attention modifications) and model compression techniques (e.g., pruning, quantization, distillation). It evaluates their interaction, robustness to compression, and the efficiency-accuracy trade-off across different networks and tasks.

Supervisors: Irene Amerini (amerini@diag.uniroma1.it), Claudio Schiavella (schiavella@diag.uniroma1.it), Lorenzo Cirillo (cirilo@diag.uniroma1.it)  

References:

1. Papa et al., A Survey on Efficient Vision Transformers: Algorithms, Techniques, and Performance Benchmarking, 2023.

2. Schiavella et al.,Optimize vision transformer architecture via efficient attention modules: a study on the monocular depth estimation task. 2024.

Simulation of a Mulberry Plantation or Silkworm Farming

Keywords: Dataset development, 3D simulation, Green AI

Abstract: This thesis proposes the development of a 3D dataset for agricultural applications, featuring both point clouds and 3D mesh models. The dataset will support use cases such as simulation, virtual and augmented reality, and machine learning in smart farming scenarios.

In collaboration with: Tecnoseta SRL (https://www.tecnoseta.com/)

Supervisors: Irene Amerini (amerini@diag.uniroma1.it), Claudia Melis Tonti (melistonti@diag.uniroma1.it), Claudio Schiavella (schiavella@diag.uniroma1.it)

References:

1. Chang et al., Shapenet: An information-rich 3d model repository, 2015.

Semi-fragile Watermarks for Generative AI Authenticity 

Keywords: Watermarking, Generative AI authenticity, Multimedia forensics 

Abstract: This thesis investigates how the implementation of semi-fragile watermarking affects the detection of AI-generated visual media. It evaluates the effectiveness of semi-fragile watermarks in the context of generative content authenticity and the robustness under backdoor attacks and data poisoning.

Supervisors: Irene Amerini (amerini@diag.uniroma1.it), Giuseppe Daidone (giuseppe.daidone@uniroma1.it)

References: 

1. Deng, J., Lin, C., Zhao, Z., Liu, S., Peng, Z., Wang, Q., & Shen, C. (2025). A Survey of Defenses Against AI-Generated Visual Media: Detection, Disruption, and Authentication. ACM Computing Surveys. https://doi.org/10.1145/3770916 

2. Yuan, Z., Zhang, X., Wang, Z., & Yin, Z. (2024). Semi-fragile neural network watermarking for content authentication and tampering localization. Expert Systems with Applications, 236, 121315–121315. https://doi.org/10.1016/j.eswa.2023.121315