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RaPA: Enhancing Transferable Targeted Attacks via Random Parameter Pruning
🤖AI Summary
Researchers propose Random Parameter Pruning Attack (RaPA), a new method that improves targeted adversarial attacks by randomly pruning model parameters during optimization. The technique achieves up to 11.7% higher attack success rates when transferring from CNN to Transformer models compared to existing methods.
Key Takeaways
- →RaPA introduces parameter-level randomization to generate more transferable adversarial examples across different AI model architectures
- →The method addresses the over-reliance on small subsets of surrogate model parameters that limits attack transferability
- →RaPA achieves 11.7% higher average attack success rates than state-of-the-art baselines in CNN-to-Transformer transfers
- →The technique is training-free, cross-architecture efficient, and easily integrates into existing attack frameworks
- →Parameter pruning acts as an importance-equalization regularizer to improve adversarial example diversity
#adversarial-attacks#ai-security#machine-learning#model-transferability#parameter-pruning#cnn#transformer#cybersecurity
Read Original →via arXiv – CS AI
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