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

Researchers propose methods to attack and defend continuous data summarization systems by exploiting vulnerabilities in similarity-based perturbations through DR-submodular optimization. The work demonstrates that adversarial attacks on upstream data processing can compromise trustworthy AI pipelines and proposes defense mechanisms with theoretical guarantees.

This research addresses a critical vulnerability in AI systems that extends beyond model robustness to encompass upstream data processing infrastructure. Data summarization acts as a foundational component that filters which information reaches downstream learning systems, making it an attractive attack vector for adversaries seeking to degrade overall performance through minimal interventions. The authors formulate the problem mathematically using DR-submodularity, establishing that multi-resolution image summarization can be modeled as multilinear extensions of submodular set functions.

The work emerges from growing recognition that trustworthy AI requires end-to-end security rather than isolated defenses at the model level. Adversarial robustness research has predominantly focused on final classifiers, leaving data pipelines largely unexamined despite their deterministic influence on downstream outcomes. This gap is particularly significant in production systems where data flows through multiple preprocessing stages before reaching decision-making models.

The practical implications span multiple domains. Organizations deploying AI systems in sensitive applications—healthcare, finance, autonomous systems—now face attacks that operate earlier in the pipeline, potentially requiring defenses at the infrastructure level rather than just model level. The authors' multi-target attack formulation demonstrates that a single perturbation can degrade multiple summarization objectives simultaneously, amplifying impact efficiency.

Future work should focus on deploying these theoretical defenses in real-world production pipelines and examining how summarization robustness impacts downstream task performance under realistic threat models. The parameter sensitivity findings on real data suggest practical implementation will require careful tuning for specific domains.

• →Data summarization systems are vulnerable to adversarial attacks that compromise information flow before it reaches predictive models.
• →Multi-target attack formulations enable adversaries to degrade multiple summarization objectives with a single perturbation.
• →Robust defenses show theoretical guarantees but require domain-specific parameter tuning for real-world effectiveness.
• →Trustworthy AI requires security measures across entire pipelines, not just final model layers.
• →Attack budgets in low-to-moderate ranges prove effective, making upstream defenses operationally critical.