This work introduces a novel foundation model for TAG anomaly detection featuring decoupled topological and textual prototypes and constructs dual prototype banks to independently model structural normality and semantic consistency, effectively isolating anomaly cues that are otherwise diluted during coupled aggregation.
Ziyang Wang, Liwen Wu, Cheng Xie et al.· 0 citations
Deepneural networks are highly vulnerable to adversarial examples, which are generatedby introducing subtle perturbations to input data to mislead model classification. Currently,transfer-based attacks are prevalent in adversarial example generation and canbe categorized into input transformation-based and gradient-based methods. However,most input transformation-based methods tend to produce augmented replicas that aresemantically inconsistent with the original inputs, while gradient-based methods oftenleave low-gradient regions unperturbed within the model’s critical attention areas. Theselimitations constrain further improvements in adversarial transferability. In this work, wepropose a Semantic-Invariance and Low-Gradient Replacement Method (SLRM) to addressthese challenges. Our framework integrates semantically consistent augmentation andgradient replacement as follows: (1) a feature extractor captures semantic features fromoriginal inputs and a reconstructor generates augmented replicas that preserve semanticfidelity to enhance input diversity, and (2) low-gradient regions in adversarial examplesare systematically replaced with corresponding regions from augmented replicas to eliminatethe under-perturbed areas critical for model robustness. Comprehensive empiricalevaluations on ImageNet demonstrate that SLRM significantly enhances the transferabilityof baseline methods and seamlessly integrates with state-of-the-art approaches to furtherimprove their performance. Moreover, SLRM substantially improves the robustness ofbaseline methods against defended models, achieving superior attack success rates underadvanced adversarial defenses.
Yunqi Zhang, Wei Chen, Lei Zhao et al.· Mathematics· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
