2 citations · 2 across the 4 of their papers we have counts for
6 papers
Quiet Triggers, Loud Footprints: A Tri-Space Measurement Study of Backdoor Stealthiness
Behrad Tajalli, Henk Berendsen, Xiaoyun Xu +1
Backdoor attacks on deep neural networks are commonly evaluated by attack success rate and benign accuracy, yet their stealthiness remains inconsistently defined and measured. Exis…
SoK: The Last Line of Defense: On Backdoor Defense Evaluation
Gorka Abad, Marina Krček, Stefanos Koffas +7
Backdoor attacks pose a significant threat to deep learning models by implanting hidden vulnerabilities that can be activated by malicious inputs. While numerous defenses have been…
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…
Backdoor Attacks on Transformers for Tabular Data: An Empirical Study
Bart Pleiter, Behrad Tajalli, Stefanos Koffas +4
Deep Neural Networks (DNNs) have shown great promise in various domains. However, vulnerabilities associated with DNN training, such as backdoor attacks, are a significant concern.…
On Feasibility of Server-side Backdoor Attacks on Split Learning
Behrad Tajalli, Oguzhan Ersoy, Stjepan Picek
Split learning is a collaborative learning design that allows several participants (clients) to train a shared model while keeping their datasets private. Recent studies demonstrat…
SoK: A Systematic Evaluation of Backdoor Trigger Characteristics in Image Classification
Gorka Abad, Jing Xu, Stefanos Koffas +3
Deep learning achieves outstanding results in many machine learning tasks. Nevertheless, it is vulnerable to backdoor attacks that modify the training set to embed a secret functio…