Attacking and Defending Printer Source Attribution Classifiers in the Physical Domain

The security of machine learning classifiers has received increasing attention in the last years. In forensic applications, guaranteeing the security of the tools investigators rely on is crucial, since the gathered evidence may be used to decide about the innocence or the guilt of a suspect. Several adversarial attacks were proposed to assess such security, with a few works focusing on transferring such attacks from the digital to the physical domain. In this work, we focus on physical d...

BioGNN: How Graph Neural Networks Can Solve Biological Problems

Graph Neural Networks (GNNs) have known an important and fast development in the last decade, with many theoretical and practical innovations. Their main feature is the capability of processing graph structured data with minimal loss of structural information. This makes GNNs the ideal family of models for processing a wide variety of biological data: metabolic networks, structural formulas of molecules, and proteins are all examples of biological data that are naturally represented as graphs...

Learning Grasping Strategies for a Soft Non-Anthropomorphic Hand from Human Demonstrations

Finding effective grasp strategies constitutes one of the main challenges in robotic manipulation, especially when dealing with soft, underactuated, and non-anthropomorphic hands. This work presents a Learning from Demonstration approach to extract grasp primitives using a novel reconfigurable soft hand, the Soft ScoopGripper (SSG). Starting from human demonstrations, we derived Gaussian models through which we were able to devise different grasping strategies, exploiting the SSG features. As...

Scienze cognitive e sociotecnica