arXiv:2606.19652v1 Announce Type: new Abstract: In this work, we introduce a training procedure for shallow neural networks that promotes robustness against adversarial attacks. We solve a non-convex Lipschitz-regularized training program by introducing a convex restriction that can be efficiently solved to global optimality. Our approach can be employed as a post-processing step by taking a pre-trained network as an initial solution to then solving the convex program whose optimal network is guaranteed to be no worse than the initial one. We illustrate the improvements of our training procedu
The continuous pursuit of more robust and secure AI systems, especially against adversarial attacks, drives innovation in training methodologies.
This work introduces a novel, convex approach to training Lipschitz-regularized shallow neural networks, offering improved robustness and efficient global optimality.
The ability to post-process pre-trained networks with a convex optimization guarantees improved robustness without performance degradation, simplifying integration for developers.
- · AI developers
- · Cybersecurity
- · Neural network researchers
- · Adversarial attackers
- · Systems highly vulnerable to AI attacks
AI models become inherently more resilient to malicious inputs, reducing the attack surface for AI-driven systems.
Increased trust in AI applications, particularly in safety-critical domains where robustness is paramount.
Accelerated adoption of AI in sensitive sectors as reliability and security concerns are partially mitigated via more robust training methods.
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Read at arXiv cs.LG