:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yun, Zebin, Weingarten, Achi-Or, Ronen, Eyal, Sharif, Mahmood
Format:	Preprint
Published:	2023
Subjects:	Computer Vision and Pattern Recognition
Online Access:	https://arxiv.org/abs/2312.11309
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Sy-FAR: Symmetry-based Fair Adversarial Robustness
by: Najjar, Haneen, et al.
Published: (2025)

NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models
by: Goren, Nir, et al.
Published: (2025)

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

Attention-aggregated Attack for Boosting the Transferability of Facial Adversarial Examples
by: Li, Jian-Wei, et al.
Published: (2025)

Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size
by: Liu, Junlin, et al.
Published: (2024)

SegTrans: Transferable Adversarial Examples for Segmentation Models
by: Song, Yufei, et al.
Published: (2025)

Adversarial Example Soups: Improving Transferability and Stealthiness for Free
by: Yang, Bo, et al.
Published: (2024)

Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
by: Xu, Nuo, et al.
Published: (2022)

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

Boosting Adversarial Transferability with Spatial Adversarial Alignment
by: Chen, Zhaoyu, et al.
Published: (2025)

A Survey on Transferability of Adversarial Examples across Deep Neural Networks
by: Gu, Jindong, et al.
Published: (2023)

Enhancing Transferability of Targeted Adversarial Examples: A Self-Universal Perspective
by: Peng, Bowen, et al.
Published: (2024)

Latent Transfer Attack: Adversarial Examples via Generative Latent Spaces
by: Shaar, Eitan, et al.
Published: (2026)

Transferable Adversarial Examples with Bayes Approach
by: Fan, Mingyuan, et al.
Published: (2022)

PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples
by: Liu, Junyu, et al.
Published: (2025)

Boosting Adversarial Transferability via High-Frequency Augmentation and Hierarchical-Gradient Fusion
by: Zheng, Yayin, et al.
Published: (2025)

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

Boosting Imperceptibility of Stable Diffusion-based Adversarial Examples Generation with Momentum
by: Haque, Nashrah, et al.
Published: (2024)

Harnessing the Computation Redundancy in ViTs to Boost Adversarial Transferability
by: Liu, Jiani, et al.
Published: (2025)

Boosting Adversarial Transferability via Commonality-Oriented Gradient Optimization
by: Gao, Yanting, et al.
Published: (2025)

Analyzing Physical Adversarial Example Threats to Machine Learning in Election Systems
by: Kamal, Khaleque Md Aashiq, et al.
Published: (2026)

On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning
by: Satou, Hana, et al.
Published: (2025)

DriveCombo: Benchmarking Compositional Traffic Rule Reasoning in Autonomous Driving
by: Ma, Enhui, et al.
Published: (2026)

Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation
by: Michaeli, Eyal, et al.
Published: (2024)

Going Further: Flatness at the Rescue of Early Stopping for Adversarial Example Transferability
by: Gubri, Martin, et al.
Published: (2023)

Boosting Adversarial Transferability Against Defenses via Multi-Scale Transformation
by: Guo, Zihong, et al.
Published: (2025)

Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models
by: Guo, Qi, 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)

Improving Transferability of Adversarial Examples via Bayesian Attacks
by: Li, Qizhang, et al.
Published: (2023)

Improving the Transferability of Adversarial Examples by Inverse Knowledge Distillation
by: Wu, Wenyuan, et al.
Published: (2025)

Boosting Adversarial Transferability via Ensemble Non-Attention
by: Zou, Yipeng, et al.
Published: (2025)

Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization
by: Wang, Jiafeng, et al.
Published: (2022)

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

Transfer Attack for Bad and Good: Explain and Boost Adversarial Transferability across Multimodal Large Language Models
by: Cheng, Hao, et al.
Published: (2024)

Boosting Adversarial Transferability with Low-Cost Optimization via Maximin Expected Flatness
by: Qiu, Chunlin, et al.
Published: (2024)

Group-based Robustness: A General Framework for Customized Robustness in the Real World
by: Lin, Weiran, et al.
Published: (2023)

Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation
by: Zhou, Fengfan, et al.
Published: (2024)

DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models
by: Islam, Khawar, et al.
Published: (2024)

Adversarial Examples Are Not Real Features
by: Li, Ang, et al.
Published: (2023)

ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware Diffusion Guidance
by: Chen, Yongwei, et al.
Published: (2024)