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RESQ: A Unified Framework for REliability- and Security Enhancement of Quantized Deep Neural Networks
arXiv – CS AI|Ali Soltan Mohammadi, Samira Nazari, Ali Azarpeyvand, Mahdi Taheri, Milos Krstic, Michael Huebner, Christian Herglotz, Tara Ghasempouri|
🤖AI Summary
Researchers propose RESQ, a three-stage framework that enhances both security and reliability of quantized deep neural networks through specialized fine-tuning techniques. The framework demonstrates up to 10.35% improvement in attack resilience and 12.47% in fault resilience while maintaining competitive accuracy across multiple neural network architectures.
Key Takeaways
- →RESQ framework provides unified approach to enhance both fault tolerance and adversarial attack resistance in quantized neural networks
- →Three-stage process includes adversarial fine-tuning, fault-aware training, and post-training quantization adjustments
- →Testing on ResNet18, VGG16, EfficientNet, and Swin-Tiny shows consistent performance gains across different architectures
- →Research reveals asymmetric relationship where fault resilience improvements boost adversarial resistance, but not vice versa
- →Framework maintains competitive accuracy while significantly improving robustness in quantized networks
#neural-networks#quantization#adversarial-attacks#fault-tolerance#deep-learning#ai-security#resnet#efficientnet#machine-learning#ai-robustness
Read Original →via arXiv – CS AI
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