Guardado en:
| Autores principales: | Gao, Yansong, Peng, Huaibing, Ma, Hua, Dai, Zhiyang, Wang, Shuo, Hu, Hongsheng, Fu, Anmin, Xue, Minhui |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.04853 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks
por: Fu, Anmin, et al.
Publicado: (2025)
por: Fu, Anmin, et al.
Publicado: (2025)
CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage
por: Li, Na, et al.
Publicado: (2025)
por: Li, Na, et al.
Publicado: (2025)
Decaf: Data Distribution Decompose Attack against Federated Learning
por: Dai, Zhiyang, et al.
Publicado: (2024)
por: Dai, Zhiyang, et al.
Publicado: (2024)
TruVRF: Towards Triple-Granularity Verification on Machine Unlearning
por: Zhou, Chunyi, et al.
Publicado: (2024)
por: Zhou, Chunyi, et al.
Publicado: (2024)
Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade Defense
por: Ma, Hua, et al.
Publicado: (2023)
por: Ma, Hua, et al.
Publicado: (2023)
Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning
por: Hu, Hongsheng, et al.
Publicado: (2024)
por: Hu, Hongsheng, et al.
Publicado: (2024)
Repurposing and Evaluating the (In)Feasibility of Dataset Poisoning enabled Watermarking for Contrastive Learning
por: Dai, Zhiyang, et al.
Publicado: (2026)
por: Dai, Zhiyang, et al.
Publicado: (2026)
Intellectual Property Protection for Deep Learning Model and Dataset Intelligence
por: Jiang, Yongqi, et al.
Publicado: (2024)
por: Jiang, Yongqi, et al.
Publicado: (2024)
ArmSSL: Adversarial Robust Black-Box Watermarking for Self-Supervised Learning Pre-trained Encoders
por: Jiang, Yongqi, et al.
Publicado: (2026)
por: Jiang, Yongqi, et al.
Publicado: (2026)
Empowering IoT Firmware Secure Update with Customization Rights
por: Chen, Weihao, et al.
Publicado: (2025)
por: Chen, Weihao, et al.
Publicado: (2025)
50 Shades of Deceptive Patterns: A Unified Taxonomy, Multimodal Detection, and Security Implications
por: Shi, Zewei, et al.
Publicado: (2025)
por: Shi, Zewei, et al.
Publicado: (2025)
Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecasting
por: Ma, Hua, et al.
Publicado: (2025)
por: Ma, Hua, et al.
Publicado: (2025)
Provably Unlearnable Data Examples
por: Wang, Derui, et al.
Publicado: (2024)
por: Wang, Derui, et al.
Publicado: (2024)
A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services
por: Hu, Hongsheng, et al.
Publicado: (2023)
por: Hu, Hongsheng, et al.
Publicado: (2023)
Iterative Window Mean Filter: Thwarting Diffusion-based Adversarial Purification
por: Wang, Hanrui, et al.
Publicado: (2024)
por: Wang, Hanrui, et al.
Publicado: (2024)
Detecting Adversarial Examples
por: Mumcu, Furkan, et al.
Publicado: (2024)
por: Mumcu, Furkan, et al.
Publicado: (2024)
Adversarial Example Based Fingerprinting for Robust Copyright Protection in Split Learning
por: Lin, Zhangting, et al.
Publicado: (2025)
por: Lin, Zhangting, et al.
Publicado: (2025)
Isolate Trigger: Detecting and Eliminating Adaptive Backdoor Attacks
por: Sun, Chengrui, et al.
Publicado: (2025)
por: Sun, Chengrui, et al.
Publicado: (2025)
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
por: Li, Na, et al.
Publicado: (2024)
por: Li, Na, et al.
Publicado: (2024)
Large Language Model Adversarial Landscape Through the Lens of Attack Objectives
por: Wang, Nan, et al.
Publicado: (2025)
por: Wang, Nan, et al.
Publicado: (2025)
NADD: Amplifying Noise for Effective Diffusion-based Adversarial Purification
por: Nguyen, David D., et al.
Publicado: (2026)
por: Nguyen, David D., et al.
Publicado: (2026)
Comprehensive Evaluation of Cloaking Backdoor Attacks on Object Detector in Real-World
por: Ma, Hua, et al.
Publicado: (2025)
por: Ma, Hua, et al.
Publicado: (2025)
NeuroTrace: Inference Provenance-Based Detection of Adversarial Examples
por: Hmida, Firas Ben, et al.
Publicado: (2026)
por: Hmida, Firas Ben, et al.
Publicado: (2026)
Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples
por: Han, Sicong, et al.
Publicado: (2025)
por: Han, Sicong, et al.
Publicado: (2025)
Position: Towards Resilience Against Adversarial Examples
por: Dai, Sihui, et al.
Publicado: (2024)
por: Dai, Sihui, et al.
Publicado: (2024)
From Storage to Steering: Memory Control Flow Attacks on LLM Agents
por: Xu, Zhenlin, et al.
Publicado: (2026)
por: Xu, Zhenlin, et al.
Publicado: (2026)
NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples
por: Pu, Shi, et al.
Publicado: (2025)
por: Pu, Shi, et al.
Publicado: (2025)
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks
por: Park, Seonhye, et al.
Publicado: (2022)
por: Park, Seonhye, et al.
Publicado: (2022)
TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN
por: Liu, Ziyi, et al.
Publicado: (2024)
por: Liu, Ziyi, et al.
Publicado: (2024)
Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack
por: Li, Dongyang, et al.
Publicado: (2024)
por: Li, Dongyang, et al.
Publicado: (2024)
Enhancing Adversarial Example Detection Through Model Explanation
por: Ma, Qian, et al.
Publicado: (2025)
por: Ma, Qian, et al.
Publicado: (2025)
Masked Language Model Based Textual Adversarial Example Detection
por: Zhang, Xiaomei, et al.
Publicado: (2023)
por: Zhang, Xiaomei, et al.
Publicado: (2023)
DeepiSign-G: Generic Watermark to Stamp Hidden DNN Parameters for Self-contained Tracking
por: Abuadbba, Alsharif, et al.
Publicado: (2024)
por: Abuadbba, Alsharif, et al.
Publicado: (2024)
Improving Sustainability of Adversarial Examples in Class-Incremental Learning
por: Liu, Taifeng, et al.
Publicado: (2025)
por: Liu, Taifeng, et al.
Publicado: (2025)
Transferability Ranking of Adversarial Examples
por: Levy, Mosh, et al.
Publicado: (2022)
por: Levy, Mosh, et al.
Publicado: (2022)
BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning
por: Lu, Bingguang, et al.
Publicado: (2025)
por: Lu, Bingguang, et al.
Publicado: (2025)
Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks
por: Kozák, Matouš, et al.
Publicado: (2025)
por: Kozák, Matouš, et al.
Publicado: (2025)
Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
por: Xue, Jing, et al.
Publicado: (2025)
por: Xue, Jing, et al.
Publicado: (2025)
Reconstruction of Differentially Private Text Sanitization via Large Language Models
por: Pang, Shuchao, et al.
Publicado: (2024)
por: Pang, Shuchao, et al.
Publicado: (2024)
Creating Valid Adversarial Examples of Malware
por: Kozák, Matouš, et al.
Publicado: (2023)
por: Kozák, Matouš, et al.
Publicado: (2023)
Ejemplares similares
-
Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks
por: Fu, Anmin, et al.
Publicado: (2025) -
CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage
por: Li, Na, et al.
Publicado: (2025) -
Decaf: Data Distribution Decompose Attack against Federated Learning
por: Dai, Zhiyang, et al.
Publicado: (2024) -
TruVRF: Towards Triple-Granularity Verification on Machine Unlearning
por: Zhou, Chunyi, et al.
Publicado: (2024) -
Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade Defense
por: Ma, Hua, et al.
Publicado: (2023)