arXiv:2605. 31219v2 Announce Type: replace-cross Abstract: While decision-based black-box adversarial attacks present a severe security threat, current methodologies suffer from fundamental limitations.
By Ei Hmue Khine, Yao Li, Jiebao Sun, Shengzhu Shi, Zhichang Guo, Boying Wu
arXiv:2607. 07089v1 Announce Type: new Abstract: Relational Deep Learning (RDL) has become a standard methodology for machine learning on relational databases: the database is encoded as a heterogeneous temporal graph in which tuples become nodes and primary-key to foreign-key (PK-FK) dependencies become typed edges, over which a graph neural network is trained for downstream prediction.
By Alan Gany, Bogdan Cautis, Silviu Maniu
Relational Deep Learning (RDL) has become a standard methodology for machine learning on relational databases: the database is encoded as a heterogeneous temporal graph in which tuples become nodes and primary-key to foreign-key (PK-FK) dependencies become typed edges, over which a graph neural network is trained for downstream prediction. We study the adversarial robustness of this pipeline.
arXiv:2607. 14737v1 Announce Type: cross Abstract: Vision-language pre-trained models (VLPs) are widely used in real-world applications.
By Afsaneh Hasanebrahimi, Hanxun Huang, Christopher Leckie, James Bailey, Sarah Erfani
arXiv:2412. 08394v2 Announce Type: replace Abstract: Deep neural networks (DNNs) are vulnerable to adversarial samples crafted by adding imperceptible perturbations to clean data, potentially leading to incorrect and dangerous predictions.
By Shuhai Zhang, Jiahao Yang, Hui Luo, Jie Chen, Li Wang, Feng Liu, Bo Han, Mingkui Tan
The paper introduces Manifold Anchored Bilevel Transfer (MABT), a framework that aligns adversarial attack trajectories with the intrinsic data manifold to reduce surrogate-specific overfitting. MABT employs a relaxed manifold-anchoring operator as a semantic rectifier and formulates transfer attack generation as a distributional bilevel optimization problem, learning geometry-aligned initializations that minimize expected transfer risk. A Hessian-free solver with linear-time complexity is developed to efficiently solve the resulting hierarchy, and experiments show improved transferability across 10 attackers, 28 configurations, various victim models, and defense mechanisms.
By Yaohua Liu, Yifan Guo, Jiaxin Gao