RaPA: Enhancing Transferable Targeted Attacks via Random Parameter Pruning
📰 ArXiv cs.AI
RaPA enhances transferable targeted attacks via random parameter pruning, improving attack success rates
Action Steps
- Identify the surrogate model parameters that are most influential in generating adversarial examples
- Apply random parameter pruning to reduce the dimensionality of the parameter space and improve the transferability of the attack
- Evaluate the effectiveness of the RaPA method in improving the attack success rates compared to existing methods
- Investigate the potential applications and implications of RaPA in real-world scenarios, such as image classification and object detection
Who Needs to Know This
AI researchers and engineers working on adversarial attacks and defense mechanisms can benefit from this research to improve the robustness of their models, and security teams can use this knowledge to enhance their threat detection systems
Key Insight
💡 Random parameter pruning can enhance the transferability of targeted attacks by reducing the reliance on a small subset of surrogate model parameters
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💡 RaPA improves transferable targeted attacks via random parameter pruning #AI #AdversarialAttacks
Key Takeaways
RaPA enhances transferable targeted attacks via random parameter pruning, improving attack success rates
Full Article
Title: RaPA: Enhancing Transferable Targeted Attacks via Random Parameter Pruning
Abstract:
arXiv:2504.18594v3 Announce Type: replace-cross Abstract: Compared to untargeted attacks, targeted transfer-based attack is still suffering from much lower Attack Success Rates (ASRs), although significant improvements have been achieved by kinds of methods, such as diversifying input, stabilizing the gradient, and re-training surrogate models. In this paper, we find that adversarial examples generated by existing methods rely heavily on a small subset of surrogate model parameters, which in tur
Abstract:
arXiv:2504.18594v3 Announce Type: replace-cross Abstract: Compared to untargeted attacks, targeted transfer-based attack is still suffering from much lower Attack Success Rates (ASRs), although significant improvements have been achieved by kinds of methods, such as diversifying input, stabilizing the gradient, and re-training surrogate models. In this paper, we find that adversarial examples generated by existing methods rely heavily on a small subset of surrogate model parameters, which in tur
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