6 papers
Backdoor Mitigation in Object Detection via Adversarial Fine-Tuning
Kealan Dunnett, Reza Arablouei, Dimity Miller +2
Backdoor attacks can implant malicious behaviours into deep models while preserving performance on clean data, posing a serious threat to safety-critical vision systems. Although b…
BadDet+: Robust Backdoor Attacks for Object Detection
Kealan Dunnett, Reza Arablouei, Dimity Miller +2
Backdoor attacks pose a severe threat to deep learning, yet their impact on object detection remains poorly understood compared to image classification. While attacks have been pro…
Backdoor Mitigation via Invertible Pruning Masks
Kealan Dunnett, Reza Arablouei, Dimity Miller +2
Model pruning has gained traction as a promising defense strategy against backdoor attacks in deep learning. However, existing pruning-based approaches often fall short in accurate…
Privacy Preserving Charge Location Prediction for Electric Vehicles
Robert Marlin, Raja Jurdak, Alsharif Abuadbba +1
By 2050, electric vehicles (EVs) are projected to account for 70% of global vehicle sales. While EVs provide environmental benefits, they also pose challenges for energy generation…
Countering Backdoor Attacks in Image Recognition: A Survey and Evaluation of Mitigation Strategies
Kealan Dunnett, Reza Arablouei, Dimity Miller +2
The widespread adoption of deep learning across various industries has introduced substantial challenges, particularly in terms of model explainability and security. The inherent c…
Unlearning Backdoor Attacks through Gradient-Based Model Pruning
Kealan Dunnett, Reza Arablouei, Dimity Miller +2
In the era of increasing concerns over cybersecurity threats, defending against backdoor attacks is paramount in ensuring the integrity and reliability of machine learning models.…