:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yi, Hangjie, Ming, Yuhang, Liu, Dongjun, Kong, Wanzeng
Format:	Preprint
Published:	2024
Subjects:	Cryptography and Security
Online Access:	https://arxiv.org/abs/2403.10021
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Imperceptible Adversarial Attacks for Time Series Classification with Local Perturbations and Frequency Analysis
by: Gu, Wenwei, et al.
Published: (2025)

Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis
by: He, Zhipeng, et al.
Published: (2024)

UIBDiffusion: Universal Imperceptible Backdoor Attack for Diffusion Models
by: Han, Yuning, et al.
Published: (2024)

PGD-Imp: Rethinking and Unleashing Potential of Classic PGD with Dual Strategies for Imperceptible Adversarial Attacks
by: Li, Jin, et al.
Published: (2024)

TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack
by: Wang, Yanyun, et al.
Published: (2022)

Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification
by: Ma, Jie, et al.
Published: (2025)

Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field
by: Tang, Keke, et al.
Published: (2024)

Impart: An Imperceptible and Effective Label-Specific Backdoor Attack
by: Zhao, Jingke, et al.
Published: (2024)

Robust and Imperceptible Black-box DNN Watermarking Based on Fourier Perturbation Analysis and Frequency Sensitivity Clustering
by: Liu, Yong, et al.
Published: (2022)

Towards Imperceptible Adversarial Defense: A Gradient-Driven Shield against Facial Manipulations
by: Li, Yue, et al.
Published: (2025)

UWBAD: Towards Effective and Imperceptible Jamming Attacks Against UWB Ranging Systems with COTS Chips
by: Yang, Yuqiao, et al.
Published: (2024)

Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications
by: Zhang, Quan, et al.
Published: (2024)

Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks
by: Gu, Ruizhe, et al.
Published: (2020)

IPBA: Imperceptible Perturbation Backdoor Attack in Federated Self-Supervised Learning
by: Wang, Jiayao, et al.
Published: (2025)

IU: Imperceptible Universal Backdoor Attack
by: Lin, Hsin, et al.
Published: (2026)

Imperceptible Sample-Specific Backdoor to DNN with Denoising Autoencoder
by: Wang, Xiangqi, et al.
Published: (2023)

Radio Adversarial Attacks on EMG-based Gesture Recognition Networks
by: Xie, Hongyi
Published: (2025)

D-CAPTCHA++: A Study of Resilience of Deepfake CAPTCHA under Transferable Imperceptible Adversarial Attack
by: Nguyen-Le, Hong-Hanh, et al.
Published: (2024)

Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization
by: Yang, Yulong, et al.
Published: (2023)

Selective Masking Adversarial Attack on Automatic Speech Recognition Systems
by: Fang, Zheng, et al.
Published: (2025)

LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection
by: Tian, Jiwei, et al.
Published: (2024)

Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients
by: Yuan, Jinsheng, et al.
Published: (2025)

Erasing Radio Frequency Fingerprints via Active Adversarial Perturbation
by: Lu, Zhaoyi, et al.
Published: (2024)

Deep Learning Models for Detecting Malware Attacks
by: Maniriho, Pascal, et al.
Published: (2022)

Tit-for-Tat: Safeguarding Large Vision-Language Models Against Jailbreak Attacks via Adversarial Defense
by: Hao, Shuyang, et al.
Published: (2025)

Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks
by: Zhang, Lei, et al.
Published: (2024)

Energy-Latency Attacks: A New Adversarial Threat to Deep Learning
by: Meftah, Hanene F. Z. Brachemi, et al.
Published: (2025)

Adversarial Attacks on Reinforcement Learning Agents for Command and Control
by: Dabholkar, Ahaan, et al.
Published: (2024)

A Comprehensive Review of Adversarial Attacks on Machine Learning
by: Ahmed, Syed Quiser, et al.
Published: (2024)

Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks
by: Shahgir, Haz Sameen, et al.
Published: (2023)

Imperceptible Jailbreaking against Large Language Models
by: Gao, Kuofeng, et al.
Published: (2025)

SwitchPatch: Physical Adversarial Attack Strategy with Switchable Adversarial Objectives
by: Jiang, Hanrui, et al.
Published: (2025)

Adversarial Attacks and Defenses for Speech Recognition Systems
by: Żelasko, Piotr, et al.
Published: (2021)

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
by: Xue, Jing, et al.
Published: (2025)

A Practical Adversarial Attack against Sequence-based Deep Learning Malware Classifiers
by: Tan, Kai, et al.
Published: (2025)

AdaDoS: Adaptive DoS Attack via Deep Adversarial Reinforcement Learning in SDN
by: Shao, Wei, et al.
Published: (2025)

PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
by: Feng, Chen, et al.
Published: (2024)

ReLATE+: Unified Framework for Adversarial Attack Detection, Classification, and Resilient Model Selection in Time-Series Classification
by: Kocal, Cagla Ipek, et al.
Published: (2025)

A Plug-and-Play Method for Improving Imperceptibility and Capacity in Practical Generative Text Steganography
by: Pang, Kaiyi
Published: (2024)

Multi-Screaming-Channel Attacks: Frequency Diversity for Enhanced Attacks
by: Guillaume, Jeremy, et al.
Published: (2025)