AdversarialTensors

Software · Tue Sep 12 20:00:00 EDT 2023

DOI:https://doi.org/10.11578/dc.20230922.1· OSTI ID:code-113757 · Code ID:113757

Bhattarai, Manish; Alexandrov, Boian; Rasmussen, Kim; Nebgen, Benjamin; Kaymak, Mehmet

This library builds a framework for defending ML models against adversarial attacks. The library will be developed at various stages leading to publication and software release at each stage. We employ tensor decomposition strategies as preprocessing stages for the first stage to provide robustness against the prominent adversarial noise. In the second stage, we develop a latent noise generator capable of generating novel adversarial noise that threatens the existing state-of-the-art defense strategy. In the third stage, we develop a UNSUP-GAN model, where the generator is trained to denoise against latent noise and most adversarial noises. This generator can provide a robust adversarial attack against any unseen attack.

• Site Accession Number: C23056
• Software Type: Scientific
• License(s): BSD 3-clause "New" or "Revised" License
• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program

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  Primary Award/Contract Number:

  AC52-06NA25396
• DOE Contract Number: AC52-06NA25396
• Code ID: 113757
• OSTI ID: code-113757
• Country of Origin: United States