Toward Trustworthy AI: Multi-Target Adversarial Attacks and Robust Defenses for Continuous Data Summarization

Data summarization is a critical vulnerability in AI systems: The paper demonstrates that adversarial attacks targeting upstream data summarization processes can compromise trustworthy AI by altering which information gets passed to downstream decision-making systems, not just by attacking the final predictive models themselves.

Small perturbations can have outsized effects: The research shows that adversarial perturbations to similarity structures in data summarization—even within "low-to-moderate budget regimes"—can effectively degrade the representativeness of summaries and cause measurable performance losses in subsequent learning tasks.

Defense mechanisms are feasible but require proactive design: The paper proposes robust defense strategies formulated as regularized optimization problems that can mitigate multiple types of attacks simultaneously, suggesting that trustworthy AI systems need deliberate defensive measures built into data processing pipelines, not added as afterthoughts.

Image summarization systems are particularly vulnerable: The research specifically identifies multi-resolution image summarization objectives as susceptible to these attacks, which has practical implications for AI systems used in visual data analysis, medical imaging, or autonomous systems that rely on image processing.

Theoretical guarantees matter for real-world deployment: The paper develops approximation algorithms with theoretical performance guarantees for both attacks and defenses, indicating that responsible AI requires mathematically-grounded approaches rather than ad-hoc security measures.