SOTAVerified

Backdoor Attack

Backdoor attacks inject maliciously constructed data into a training set so that, at test time, the trained model misclassifies inputs patched with a backdoor trigger as an adversarially-desired target class.

Papers

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Showing 351–400 of 523 papers

TitleStatusHype
Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks—0
Attack On Prompt: Backdoor Attack in Prompt-Based Continual Learning—0
Backdoor Attack in the Physical World—0
Backdoor Attack on Multilingual Machine Translation—0
Backdoor Attack on Vertical Federated Graph Neural Network Learning—0
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation—0
Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger—0
Backdoor Attacks against Image-to-Image Networks—0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions—0
Backdoor Attacks in Peer-to-Peer Federated Learning—0
Backdoor Attacks on Bayesian Neural Networks using Reverse Distribution—0
Backdoor Attacks on the DNN Interpretation System—0
Backdoor Attacks with Input-unique Triggers in NLP—0
Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System—0
Backdoor Attack with Imperceptible Input and Latent Modification—0
Backdoor Attack with Mode Mixture Latent Modification—0
BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning—0
BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning—0
BackdoorBench: A Comprehensive Benchmark of Backdoor Learning—0
Backdoor Detection through Replicated Execution of Outsourced Training—0
Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented Generation of Large Language Models—0
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers—0
Backdooring Convolutional Neural Networks via Targeted Weight Perturbations—0
Backdooring Outlier Detection Methods: A Novel Attack Approach—0
Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing—0
BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning—0
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers—0
Backdoors Stuck At The Frontdoor: Multi-Agent Backdoor Attacks That Backfire—0
BadApex: Backdoor Attack Based on Adaptive Optimization Mechanism of Black-box Large Language Models—0
BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—0
BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection—0
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT—0
BadHMP: Backdoor Attack against Human Motion Prediction—0
BadLingual: A Novel Lingual-Backdoor Attack against Large Language Models—0
BadMoE: Backdooring Mixture-of-Experts LLMs via Optimizing Routing Triggers and Infecting Dormant Experts—0
BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements—0
BadNL: Backdoor Attacks Against NLP Models—0
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models—0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks—0
BadScan: An Architectural Backdoor Attack on Visual State Space Models—0
BadSFL: Backdoor Attack against Scaffold Federated Learning—0
EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events—0
BADTV: Unveiling Backdoor Threats in Third-Party Task Vectors—0
BadVFL: Backdoor Attacks in Vertical Federated Learning—0
BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization—0
BATT: Backdoor Attack with Transformation-based Triggers—0
BDMMT: Backdoor Sample Detection for Language Models through Model Mutation Testing—0
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