Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency
arXiv:2403.09441v2 Announce Type: replace Abstract: As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversarial attacks has become increasingly critical. DL models have been found to be susceptible to adversarial attacks by introducing small, targeted perturbations to disrupt the input data. Adversarial training has been presented as a mitigation strategy that can result in more robust models. This adversarial robustness comes with additional computational costs required to design adversarial attacks du
The increasing integration of deep learning models into critical applications necessitates robust and efficient AI, driving research into methods like adversarial fine-tuning.
Improving the robustness and efficiency of AI models simultaneously addresses key challenges for their widespread, secure, and resource-effective deployment in real-world scenarios.
This research suggests a pathway to developing AI models that are both more resilient to adversarial attacks and less computationally demanding, potentially broadening their applicability.
- · AI developers
- · Cybersecurity sector
- · Edge AI providers
- · Users of AI-powered systems
- · Adversarial attackers relying on current model vulnerabilities
- · Developers solely focused on robustness at the expense of efficiency
More secure and computationally efficient AI deployments become feasible across various industries.
Reduced infrastructure costs for deploying robust AI could accelerate adoption in resource-constrained environments.
The widespread deployment of robust and efficient AI may lead to new security and ethical challenges related to autonomous systems.
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Read at arXiv cs.LG