Copertina

BeniFul: Backdoor Defense via Middle Feature Analysis for Deep Neural Networks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Li, Xinfu, Zhang, Junying, Ma, Xindi
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Cryptography and Security Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Universal Graph Backdoor Defense: A Feature-based Homophily Perspective
di: Pan, Mengting, et al.
Pubblicazione: (2026)

Defending against Backdoor Attack on Deep Neural Networks
di: Cheng, Hao, et al.
Pubblicazione: (2020)

Robustness Inspired Graph Backdoor Defense
di: Zhang, Zhiwei, et al.
Pubblicazione: (2024)

Seal Your Backdoor with Variational Defense
di: Sabolić, Ivan, et al.
Pubblicazione: (2025)

BlockDoor: Blocking Backdoor Based Watermarks in Deep Neural Networks
di: Puah, Yi Hao, et al.
Pubblicazione: (2024)

FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning
di: Yan, Xinhai, et al.
Pubblicazione: (2025)

Certified Defense on the Fairness of Graph Neural Networks
di: Dong, Yushun, et al.
Pubblicazione: (2023)

PECAN: A Deterministic Certified Defense Against Backdoor Attacks
di: Zhang, Yuhao, et al.
Pubblicazione: (2023)

Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade Defense
di: Ma, Hua, et al.
Pubblicazione: (2023)

Backdoor Defense through Self-Supervised and Generative Learning
di: Sabolić, Ivan, et al.
Pubblicazione: (2024)

PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks
di: Wang, Yue, et al.
Pubblicazione: (2022)

Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks
di: Dong, Xuewen, et al.
Pubblicazione: (2025)

FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacement
di: Abacha, Fatima Z., et al.
Pubblicazione: (2026)

Cryptographic Backdoor for Neural Networks: Boon and Bane
di: Ngo, Anh Tu, et al.
Pubblicazione: (2025)

Backdoor Mitigation in Deep Neural Networks via Strategic Retraining
di: Dhonthi, Akshay, et al.
Pubblicazione: (2022)

DMGNN: Detecting and Mitigating Backdoor Attacks in Graph Neural Networks
di: Sui, Hao, et al.
Pubblicazione: (2024)

Quantization Blindspots: How Model Compression Breaks Backdoor Defenses
di: Pandey, Rohan, et al.
Pubblicazione: (2025)

BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning
di: Wu, Baoyuan, et al.
Pubblicazione: (2024)

Uncovering, Explaining, and Mitigating the Superficial Safety of Backdoor Defense
di: Min, Rui, et al.
Pubblicazione: (2024)

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients
di: Zhang, Jianyi, et al.
Pubblicazione: (2025)

FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning
di: Yang, Yanxin, et al.
Pubblicazione: (2023)

TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learning
di: Zhang, Mingxuan, et al.
Pubblicazione: (2025)

Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models
di: Kalavasis, Alkis, et al.
Pubblicazione: (2024)

Backdoor Vectors: a Task Arithmetic View on Backdoor Attacks and Defenses
di: Pawlak, Stanisław, et al.
Pubblicazione: (2025)

Data-centric NLP Backdoor Defense from the Lens of Memorization
di: Wang, Zhenting, et al.
Pubblicazione: (2024)

Patch Synthesis for Property Repair of Deep Neural Networks
di: Chi, Zhiming, et al.
Pubblicazione: (2024)

TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks
di: Hossain, Khondoker Murad, et al.
Pubblicazione: (2024)

Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
di: Gosch, Lukas, et al.
Pubblicazione: (2024)

Backdoor Channels Hidden in Latent Space: Cryptographic Undetectability in Modern Neural Networks
di: Eggen, Marte, et al.
Pubblicazione: (2026)

TrojanDam: Detection-Free Backdoor Defense in Federated Learning through Proactive Model Robustification utilizing OOD Data
di: Dai, Yanbo, et al.
Pubblicazione: (2025)

Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors
di: Kadir, Md Abdul, et al.
Pubblicazione: (2024)

CodePurify: Defend Backdoor Attacks on Neural Code Models via Entropy-based Purification
di: Mu, Fangwen, et al.
Pubblicazione: (2024)

DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis
di: Hmida, Firas Ben, et al.
Pubblicazione: (2025)

ArcGen: Generalizing Neural Backdoor Detection Across Diverse Architectures
di: Yang, Zhonghao, et al.
Pubblicazione: (2025)

DiffusionHijack: Supply-Chain PRNG Backdoor Attack on Diffusion Models and Quantum Random Number Defense
di: You, Ziyang, et al.
Pubblicazione: (2026)

Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning
di: Luo, Xinjian, et al.
Pubblicazione: (2020)

ReVeil: Unconstrained Concealed Backdoor Attack on Deep Neural Networks using Machine Unlearning
di: Alam, Manaar, et al.
Pubblicazione: (2025)

Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation
di: Ngo, Anh Tu, et al.
Pubblicazione: (2025)

Oblivious Defense in ML Models: Backdoor Removal without Detection
di: Goldwasser, Shafi, et al.
Pubblicazione: (2024)

Mitigating Deep Reinforcement Learning Backdoors in the Neural Activation Space
di: Vyas, Sanyam, et al.
Pubblicazione: (2024)