12 citations · 30 across the 16 of their papers we have counts for
20 papers
C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift
Davide Domini, Gianluca Aguzzi, Lorenzo Pellegrini +2
Collective Adaptive Systems (CAS) increasingly rely on machine learning to let each node learn from locally sensed data, aligning its behavior with the surrounding environment. Sca…
Detecting Localized Deepfakes: How Well Do Synthetic Image Detectors Handle Inpainting?
Serafino Pandolfini, Lorenzo Pellegrini, Matteo Ferrara +1
The rapid progress of generative AI has enabled highly realistic image manipulations, including inpainting and region-level editing. These approaches preserve most of the original…
Generalized Design Choices for Deepfake Detectors
Lorenzo Pellegrini, Serafino Pandolfini, Davide Maltoni +3
The effectiveness of deepfake detection methods often depends less on their core design and more on implementation details such as data preprocessing, augmentation strategies, and…
AI-GenBench: A New Ongoing Benchmark for AI-Generated Image Detection
Lorenzo Pellegrini, Davide Cozzolino, Serafino Pandolfini +5
The rapid advancement of generative AI has revolutionized image creation, enabling high-quality synthesis from text prompts while raising critical challenges for media authenticity…
Continual Learning in the Presence of Repetition
Hamed Hemati, Lorenzo Pellegrini, Xiaotian Duan +11
Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world p…
Continual Learning by Three-Phase Consolidation
Davide Maltoni, Lorenzo Pellegrini
TPC (Three-Phase Consolidation) is here introduced as a simple but effective approach to continually learn new classes (and/or instances of known classes) while controlling forgett…