37 citations · 224 across the 83 of their papers we have counts for
11 papers · 1 filter
CatBack: Universal Backdoor Attacks on Tabular Data via Categorical Encoding
Behrad Tajalli, Stefanos Koffas, Stjepan Picek
Backdoor attacks in machine learning have drawn significant attention for their potential to compromise models stealthily, yet most research has focused on homogeneous data such as…
BAN: Detecting Backdoors Activated by Adversarial Neuron Noise
Xiaoyun Xu, Zhuoran Liu, Stefanos Koffas +2
Backdoor attacks on deep learning represent a recent threat that has gained significant attention in the research community. Backdoor defenses are mainly based on backdoor inversio…
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning
Marco Arazzi, Stefanos Koffas, Antonino Nocera +1
Federated Transfer Learning (FTL) is the most general variation of Federated Learning. According to this distributed paradigm, a feature learning pre-step is commonly carried out b…
Momentum Gradient-based Untargeted Attack on Hypergraph Neural Networks
Yang Chen, Stjepan Picek, Zhonglin Ye +2
Hypergraph Neural Networks (HGNNs) have been successfully applied in various hypergraph-related tasks due to their excellent higher-order representation capabilities. Recent works…
Label Inference Attacks against Node-level Vertical Federated GNNs
Marco Arazzi, Mauro Conti, Stefanos Koffas +4
Federated learning enables collaborative training of machine learning models by keeping the raw data of the involved workers private. Three of its main objectives are to improve th…
Turning Privacy-preserving Mechanisms against Federated Learning
Marco Arazzi, Mauro Conti, Antonino Nocera +1
Recently, researchers have successfully employed Graph Neural Networks (GNNs) to build enhanced recommender systems due to their capability to learn patterns from the interaction b…
Rethinking the Trigger-injecting Position in Graph Backdoor Attack
Jing Xu, Gorka Abad, Stjepan Picek
Backdoor attacks have been demonstrated as a security threat for machine learning models. Traditional backdoor attacks intend to inject backdoor functionality into the model such t…
IB-RAR: Information Bottleneck as Regularizer for Adversarial Robustness
Xiaoyun Xu, Guilherme Perin, Stjepan Picek
In this paper, we propose a novel method, IB-RAR, which uses Information Bottleneck (IB) to strengthen adversarial robustness for both adversarial training and non-adversarial-trai…
Universal Soldier: Using Universal Adversarial Perturbations for Detecting Backdoor Attacks
Xiaoyun Xu, Oguzhan Ersoy, Stjepan Picek
Deep learning models achieve excellent performance in numerous machine learning tasks. Yet, they suffer from security-related issues such as adversarial examples and poisoning (bac…
Watermarking Graph Neural Networks based on Backdoor Attacks
Jing Xu, Stefanos Koffas, Oguzhan Ersoy +1
Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications. Building a powerful GNN model is not a trivial task, as it requires a large amo…
Explainability-based Backdoor Attacks Against Graph Neural Networks
Jing Xu, Minhui, Xue +1
Backdoor attacks represent a serious threat to neural network models. A backdoored model will misclassify the trigger-embedded inputs into an attacker-chosen target label while per…