Cover Image

Disrupting Semantic and Abstract Features for Better Adversarial Transferability

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Luo, Yuyang, Wang, Xiaosen, Ge, Zhijin, He, Yingzhe
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Boosting the Local Invariance for Better Adversarial Transferability
by: Liu, Bohan, et al.
Published: (2025)

Devling into Adversarial Transferability on Image Classification: Review, Benchmark, and Evaluation
by: Wang, Xiaosen, et al.
Published: (2026)

Attention! Your Vision Language Model Could Be Maliciously Manipulated
by: Wang, Xiaosen, et al.
Published: (2025)

Security Risk of Misalignment between Text and Image in Multi-modal Model
by: Wang, Xiaosen, et al.
Published: (2025)

Boosting Adversarial Transferability by Block Shuffle and Rotation
by: Wang, Kunyu, et al.
Published: (2023)

Bag of Tricks to Boost Adversarial Transferability
by: Zhang, Zeliang, et al.
Published: (2024)

ViT-EnsembleAttack: Augmenting Ensemble Models for Stronger Adversarial Transferability in Vision Transformers
by: Cao, Hanwen, et al.
Published: (2025)

Improving the Transferability of Adversarial Examples by Feature Augmentation
by: Wang, Donghua, et al.
Published: (2024)

Exploring Frequencies via Feature Mixing and Meta-Learning for Improving Adversarial Transferability
by: Weng, Juanjuan, et al.
Published: (2024)

TriQDef: Disrupting Semantic and Gradient Alignment to Prevent Adversarial Patch Transferability in Quantized Neural Networks
by: Guesmi, Amira, et al.
Published: (2025)

Learning to Transform Dynamically for Better Adversarial Transferability
by: Zhu, Rongyi, et al.
Published: (2024)

Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing
by: Weng, Juanjuan, et al.
Published: (2024)

Beyond Prototypes: Semantic Anchor Regularization for Better Representation Learning
by: Ge, Yanqi, et al.
Published: (2023)

For Better or For Worse? Learning Minimum Variance Features With Label Augmentation
by: Chidambaram, Muthu, et al.
Published: (2024)

Revisiting Efficient Semantic Segmentation: Learning Offsets for Better Spatial and Class Feature Alignment
by: Zhang, Shi-Chen, et al.
Published: (2025)

Enabling Heterogeneous Adversarial Transferability via Feature Permutation Attacks
by: Wu, Tao, et al.
Published: (2025)

Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack
by: Jia, Xiaojun, et al.
Published: (2024)

Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature
by: Wang, Yichen, et al.
Published: (2024)

StyDeco: Unsupervised Style Transfer with Distilling Priors and Semantic Decoupling
by: Yang, Yuanlin, et al.
Published: (2025)

MRIFE: A Mask-Recovering and Interactive-Feature-Enhancing Semantic Segmentation Network For Relic Landslide Detection
by: He, Juefei, et al.
Published: (2024)

Semantix: An Energy Guided Sampler for Semantic Style Transfer
by: He, Huiang, et al.
Published: (2025)

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization
by: Guesmi, Amira, et al.
Published: (2025)

Adversarial Attacks Against MLLMs via Progressive Resolution Processing and Adaptive Feature Alignment
by: Wang, Haobo, et al.
Published: (2026)

ICCV23 Visual-Dialog Emotion Explanation Challenge: SEU_309 Team Technical Report
by: Yuan, Yixiao, et al.
Published: (2024)

Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space
by: Diao, Yunfeng, et al.
Published: (2024)

Stain Style Transfer using Transitive Adversarial Networks
by: Cai, Shaojin, et al.
Published: (2019)

Harnessing Textual Semantic Priors for Knowledge Transfer and Refinement in CLIP-Driven Continual Learning
by: He, Lingfeng, et al.
Published: (2025)

SGHA-Attack: Semantic-Guided Hierarchical Alignment for Transferable Targeted Attacks on Vision-Language Models
by: Wang, Haobo, et al.
Published: (2026)

An Image Is Worth 1000 Lies: Adversarial Transferability across Prompts on Vision-Language Models
by: Luo, Haochen, et al.
Published: (2024)

Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features
by: Wu, Shangbo, et al.
Published: (2025)

Transfer Learning of Real Image Features with Soft Contrastive Loss for Fake Image Detection
by: Liang, Ziyou, et al.
Published: (2024)

Large AI Model-Enabled Generative Semantic Communications for Image Transmission
by: Ma, Qiyu, et al.
Published: (2025)

Exploring Adversarial Transferability between Kolmogorov-arnold Networks
by: Wang, Songping, et al.
Published: (2025)

Feature Projection Learning for Better Vision-Language Reasoning
by: Zhang, Yi, et al.
Published: (2026)

Enhancing Adversarial Transferability via Component-Wise Transformation
by: Liu, Hangyu, et al.
Published: (2025)

SITA: Structurally Imperceptible and Transferable Adversarial Attacks for Stylized Image Generation
by: Kang, Jingdan, et al.
Published: (2025)

Initialization Matters for Adversarial Transfer Learning
by: Hua, Andong, et al.
Published: (2023)

AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation
by: Zhu, Yuhan, et al.
Published: (2024)

Harmonizing Feature Maps: A Graph Convolutional Approach for Enhancing Adversarial Robustness
by: Zhang, Kejia, et al.
Published: (2024)

CLIP Brings Better Features to Visual Aesthetics Learners
by: Xu, Liwu, et al.
Published: (2023)