MiniFool -- Physics-Constraint-Aware Minimizer-Based Adversarial Attacks in Deep Neural Networks
arXiv:2511.01352v2 Announce Type: replace Abstract: In this paper, we present a new algorithm, MiniFool, that implements physics-inspired adversarial attacks for testing neural network-based classification tasks in particle and astroparticle physics. While we initially developed the algorithm for the search for astrophysical tau neutrinos with the IceCube Neutrino Observatory, we apply it to further data from other science domains, thus demonstrating its general applicability. Here, we apply the algorithm to the well-known MNIST data set and furthermore, to Open Data data from the CMS experime
This research is emerging now as the application of deep neural networks in critical scientific domains like particle physics increases, necessitating robust methods to evaluate their vulnerability and reliability.
A strategic reader should care as adversarial attacks on AI, particularly in scientific and mission-critical applications, highlight the imperative for developing physics-aware, robust AI systems, impacting national security and scientific integrity.
The development of physics-constraint-aware adversarial attacks means that AI systems built for scientific discovery and critical infrastructure will require more sophisticated validation and security protocols beyond standard benchmarks.
- · AI robustness and security researchers
- · High-energy physics collaborations
- · Defense and intelligence agencies
- · AI developers ignoring scientific constraints
- · Organizations relying on unvalidated AI models
Enhances the understanding of AI vulnerabilities in scientific applications.
Drives the development of more resilient and interpretable AI models for critical domains.
Could influence regulatory frameworks for AI deployment in scientific research and sensitive infrastructure, moving towards physics-informed validation.
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Read at arXiv cs.LG