Author

Yaohua Liu

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#machine learning Preprint Sep 2026

Anchoring Adversarial Trajectories to Data Manifolds: A Bilevel Transfer Optimization Framework

Manifold Anchored Bilevel Transfer (MABT), a unified framework that anchors adversarial trajectories to the shared semantic subspace, is proposed, and a Hessian-free solver with linear-time complexity is developed to handle the resulting hierarchy.

Yao-Hua Liu, Yi-Fan Guo, Jia-Xin Gao · 0 citations

Preprint Aug 2026

Learning with Bilevel-Minimax Optimization for Efficient and Reliable Transfer Attacks

This work proposes BMAT (Bilevel-Minimax Adversarial Transfer), an integrated bottom-up solver that combines a Soft Weight Modulator and an Implicit Gradient Approximator to enable ternary coupling among initialization, surrogate adaptation, and perturbation optimization.

Yao-Hua Liu, Yi-Fan Guo, Jia-Xin Gao · 0 citations