EarlyExodus: Leveraging early exits to mitigate backdoor vulnerability in deep learning - Salmane Douch; M. Riduan Abid; Khalid Zine-Dine; Driss Bouzidi; Fatima Ezzahra El Aidos; Driss Benhaddou (Results in Engineering, 2025)
The rapid migration of artificial-intelligence workloads toward edge computing significantly enhances capabilities in critical applications such as autonomous vehicles, augmented and virtual reality, and e-health, but it also heightens the urgency for robust security. However, this urgency reveals a critical gap: state-of-the-art backdoor defenses remain vulnerable to sophisticated data-poisoning attacks that subtly embed malicious triggers into training data and covertly manipulate model predictions, threatening the reliability and trustworthiness of edge-deployed AI.To counter this threat, we propose a defense mechanism that neutralizes advanced data poisoning attacks, clearly identifies maliciously targeted labels, and preserves model accuracy and integrity across diverse architectures and datasets. Our technique, EarlyExodus, integrates early-exit branches within neural networks and trains them with a divergence objective so that, for poisoned inputs, the early exit exposes the malicious label while the final exit maintains the correct classification. Extensive experiments on LeNet-5, ResNet-32, and GhostNet across MNIST, CIFAR-10, and GTSRB reduce the average attack success rate of seven recent backdoor attacks to about 3%, with clean-data accuracy degradation kept below 2%. These results demonstrate a practical, architecture-agnostic pathway toward trustworthy edge-AI systems and lay the foundation for extending backdoor defenses beyond image models to broader application domains.