Tag: Robustness

All the articles with the tag "Robustness".

• Adversarial Attacks in Multimodal Systems: A Practitioner's Survey

  Published: 8 May, 2025 |  at  11:09 AM

  

  96.71 🤔

  This survey paper provides a comprehensive overview of adversarial attacks on multimodal AI systems across text, image, video, and audio modalities, categorizing threats by attacker knowledge, intention, and execution to equip practitioners with knowledge of vulnerabilities and cross-modal risks.

• CB-cPIR: Code-Based Computational Private Information Retrieval

  Published: 8 May, 2025 |  at  11:08 AM

  

  93.98 🤔

  CB-cPIR introduces a code-based single-server computational private information retrieval scheme that enhances security against subquery attacks by using high-weight secret vectors and dual queries, achieving lower communication and computational costs compared to lattice-based schemes like XPIR and SimplePIR.

• Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks

  Published: 8 May, 2025 |  at  11:06 AM

  

  90.63 🤔

  This paper proposes a Dynamic Bayesian Network framework for autonomous vehicles that enhances safety in cut-in maneuvers by integrating lateral evidence and probabilistic safety assessments, achieving superior crash avoidance in high-speed scenarios (9.22% crash rate) compared to baseline models in the JRC-FSM simulator.

• MELON: Provable Indirect Prompt Injection Defense via Masked Re-execution and Tool Comparison

  Published: 8 May, 2025 |  at  10:22 AM

  

  89.40 🤔

  MELON introduces a novel training-free defense against indirect prompt injection attacks on LLM agents by detecting independence of tool calls from user inputs through masked re-execution, achieving superior attack prevention (0.24% ASR on GPT-4o) and utility preservation (58.78% UA on GPT-4o) compared to existing methods.

• Sparse-Group Boosting with Balanced Selection Frequencies: A Simulation-Based Approach and R Implementation

  Published: 8 May, 2025 |  at  10:25 AM

  

  87.75 🤔

  This paper introduces sparse-group boosting and a simulation-based group balancing algorithm within the 'sgboost' R package to mitigate variable selection bias in high-dimensional grouped data, demonstrating improved fairness and interpretability through simulations and ecological data analysis.