SensorFault-Bench is a new CPS-grounded benchmark showing that clean-MSE rankings of forecasting models often disagree with their robustness under standardized sensor-fault scenarios across four real datasets.
Towards deep learning models resistant to adversarial attacks
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
PGD²-GSM is the first method to stably achieve high-resolution global semantic manipulation in learned image compression via a Periodic Geometric Decay schedule that handles Lazying-Oscillating-Refining attack stages.
Penalty-based first-order methods find ε-KKT points in bilevel minimax problems with Õ(ε^{-4}) deterministic and Õ(ε^{-9}) stochastic oracle complexity, improving prior bounds for constrained lower-level cases via Lagrangian duality.
FragileFlow formalizes margin-aware error flow and applies spectral control through a calibrated margin buffer and class-wise risk matrix, supported by a PAC-Bayes bound, to enhance worst-class robustness in foundation model adaptation while preserving clean accuracy.
citing papers explorer
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Benchmarking Sensor-Fault Robustness in Forecasting
SensorFault-Bench is a new CPS-grounded benchmark showing that clean-MSE rankings of forecasting models often disagree with their robustness under standardized sensor-fault scenarios across four real datasets.
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Control Your View: High-Resolution Global Semantic Manipulation in Learned Image Compression
PGD²-GSM is the first method to stably achieve high-resolution global semantic manipulation in learned image compression via a Periodic Geometric Decay schedule that handles Lazying-Oscillating-Refining attack stages.
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Penalty-Based First-Order Methods for Bilevel Optimization with Minimax and Constrained Lower-Level Problems
Penalty-based first-order methods find ε-KKT points in bilevel minimax problems with Õ(ε^{-4}) deterministic and Õ(ε^{-9}) stochastic oracle complexity, improving prior bounds for constrained lower-level cases via Lagrangian duality.
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FragileFlow: Spectral Control of Correct-but-Fragile Predictions for Foundation Model Robustness
FragileFlow formalizes margin-aware error flow and applies spectral control through a calibrated margin buffer and class-wise risk matrix, supported by a PAC-Bayes bound, to enhance worst-class robustness in foundation model adaptation while preserving clean accuracy.