Interpreting Global Perturbation Robustness of Image Models using Axiomatic Spectral Importance Decomposition
arXiv:2408.01139v4 Announce Type: replace Abstract: Perturbation robustness evaluates the vulnerabilities of models, arising from a variety of perturbations, such as data corruptions and adversarial attacks. Understanding the mechanisms of perturbation robustness is critical for global interpretability. We present a model-agnostic, global mechanistic interpretability method to interpret the perturbation robustness of image models. This research is motivated by two key aspects. First, previous global interpretability works, in tandem with robustness benchmarks, e.g. mean corruption error (mCE),
The proliferation of complex AI models and increasing reliance on their outputs necessitates robust interpretability methods to ensure reliability, especially in adversarial and corrupted environments.
Understanding and improving the robustness of AI models against perturbations, whether accidental or malicious, is crucial for their deployment in sensitive applications and for building public trust.
This research provides a novel model-agnostic approach to interpret perturbation robustness globally, offering a new tool for developers to diagnose and mitigate model vulnerabilities.
- · AI developers
- · Cybersecurity experts
- · Industries deploying AI in critical infrastructure
- · Researchers in AI safety and interpretability
- · Malicious actors exploiting AI vulnerabilities
- · Organizations deploying black-box AI without robustness considerations
Improved debugging and hardening of AI models against various perturbations and attacks.
Increased adoption of interpretable and robust AI systems across industries, potentially accelerating AI integration into critical domains.
Standardization of robustness metrics and interpretability methods, fostering a more secure and trustworthy AI ecosystem.
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Read at arXiv cs.AI