arXiv:2605.31219v1 Announce Type: cross Abstract: While decision-based black-box adversarial attacks present a severe security threat, current methodologies suffer from fundamental limitations. Pixel-wise attacks frequently introduce unnatural, high-frequency visual artifacts, while latent-space frameworks are confined by the limited search space of low-dimensional manifolds and inherent reconstruction flaws. To resolve these limitations, we propose Latent Geometric Chords (LGC) for Query-Efficient Decision-Based Adversarial Attacks alongside a variant, LGC-H. At its core, LGC navigates decisi
The continuous evolution of AI models and their integration into critical systems necessitates more robust security measures, making research into adversarial attacks and defences increasingly urgent.
This research highlights the ongoing vulnerability of AI systems to sophisticated attacks and the critical need for advanced security protocols to ensure reliable and safe AI deployment.
The proposed Latent Geometric Chords (LGC) method offers a new, potentially more effective approach to query-efficient decision-based adversarial attacks, pushing the boundaries of AI security research.
- · AI security researchers
- · Organizations developing robust AI defence mechanisms
- · Cybersecurity firms
- · Organizations with vulnerable AI systems
- · Developers neglecting AI security
- · Users relying on unsecured AI applications
Improved adversarial attack techniques will put pressure on AI developers to create more resilient models.
Increased investment in AI safety and robustness will become a priority across industries.
The arms race between AI attackers and defenders could lead to more secure but potentially less accessible or slower AI systems.
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Read at arXiv cs.LG