most citedDr. Jekyll and Mr. Hyde: Two Faces of LLMs

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2024

Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning

Jiaxin Li, Marco Arazzi, Antonino Nocera +1

Source Inference Attack (SIA) in Federated Learning (FL) aims to identify which client used a target data point for local model training. It allows the central server to audit clie…

cs.CR2024

Membership Privacy Evaluation in Deep Spiking Neural Networks

Jiaxin Li, Gorka Abad, Stjepan Picek +1

Artificial Neural Networks (ANNs), commonly mimicking neurons with non-linear functions to output floating-point numbers, consistently receive the same signals of a data point duri…

cs.CR2024

EmoBack: Backdoor Attacks Against Speaker Identification Using Emotional Prosody

Coen Schoof, Stefanos Koffas, Mauro Conti +1

Speaker identification (SI) determines a speaker's identity based on their spoken utterances. Previous work indicates that SI deep neural networks (DNNs) are vulnerable to backdoor…

cs.CR2023★ 2 cited

Dr. Jekyll and Mr. Hyde: Two Faces of LLMs

Matteo Gioele Collu, Tom Janssen-Groesbeek, Stefanos Koffas +2

Large Language Models (LLMs) are being integrated into applications such as chatbots or email assistants. To prevent improper responses, safety mechanisms, such as Reinforcement Le…

cs.CR2023★ 1 cited

Invisible Threats: Backdoor Attack in OCR Systems

Mauro Conti, Nicola Farronato, Stefanos Koffas +2

Optical Character Recognition (OCR) is a widely used tool to extract text from scanned documents. Today, the state-of-the-art is achieved by exploiting deep neural networks. Howeve…