SRED: Secure and Robust Emotion Detection for Advanced Driver Assistance Systems

Guardado en:

Detalles Bibliográficos
Publicado en:	ProQuest Dissertations and Theses (2025)
Autor principal:	Rubaiyat, Nadia
Publicado:	ProQuest Dissertations & Theses
Materias:	Physiology Software Deep learning Datasets Back propagation Success Real time Monitoring systems Performance evaluation Emotions Machine learning Motivation Fatalities Embedded systems Artificial intelligence Intelligent systems Traffic accidents & safety Sensors Autonomous vehicles Neural networks Automobile safety Defense mechanisms Design Automotive engineering Computer science Transportation
Acceso en línea:	Citation/Abstract Full Text - PDF
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Descripción
Resumen:	Facial emotion detection is among the most accessible and non-intrusive techniques for assessing driver states in Advanced Driver Assistance Systems (ADAS). Facial landmarks—such as eyes, nose, and mouth—are central to emotion detection models, providing essential cues for inference. However, deep learning models employed for this task are highly susceptible to adversarial attacks, where imperceptible perturbations can lead to significant performance degradation. Gradient-based adversarial attacks, such as FGSM, BIM, PGD, are highly effective in changing the decision boundaries of the models and causing misclassifications. This thesis investigates the oftenoverlooked vulnerability of facial landmark regions and introduces SRED (Secure and Robust Emotion Detection), a novel framework tailored for real-time deployment in ADAS environments. Three state-of-the-art deep learning models—ResNet18, MobileNetV2, and EfficientNetB0—are trained on widely used datasets (KMU-FED, KDEF, and FER2013), incorporating both standard and attention-guided training paradigms. Robustness is evaluated against adversarial attacks including FGSM, BIM, and PGD, with a focus on resilience under cross-perturbation scenarios. Notably, the transferability analysis reveals that adversarial examples generated using attention-masked models are particularly damaging when applied to similarly trained models—for example, MobileNetV2’s accuracy on the KDEF dataset plummeted from 74.11% to just 7.77% under such conditions. To enhance interpretability, SRED also integrates saliency map analysis, offering insights into the critical facial regions influencing the model’s decisions.
ISBN:	9798270206666
Fuente:	ProQuest Dissertations & Theses Global