The Role of Input Dimensionality in the Emergence and Targeted Control of Adversarial Examples
arXiv:2606.26207v1 Announce Type: cross Abstract: Several theoretical works have tried to explain the adversarial vulnerability of deep neural networks through properties of high-dimensional geometry. However, the assumptions underlying these works are rarely examined empirically, and systematic evidence remains limited. In this work, we present a systematic study of the role of input dimensionality in both the emergence and the targeted control of adversarial examples. We first analyse the scope and limitations of existing theoretical frameworks based on concentration of measure, showing that
This research provides a deeper, empirically-driven understanding of adversarial examples in AI, building on existing theoretical frameworks but addressing their empirical limitations to refine current models of AI vulnerability.
Understanding the emergence and control of adversarial examples is crucial for developing robust and secure AI systems, impacting their reliability in critical applications and trust in autonomous decisions.
This research refines our understanding of AI vulnerabilities, guiding more effective development of defensive mechanisms and potentially accelerating the deployment of more resilient AI.
- · AI security researchers
- · Developers of robust AI systems
- · Sectors reliant on secure AI (e.g., defense, finance)
- · Malicious actors exploiting AI vulnerabilities
- · Developers of brittle AI systems
Improved understanding of deep neural network vulnerabilities.
Development of more effective and generalizable adversarial defense techniques.
Increased adoption of AI in high-stakes environments due to enhanced trust and security.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG