BadBone backdoors backbone models with bi-level optimization to make prompt learning on downstream tasks vulnerable while preserving model utility.
arXiv preprint arXiv:1908.01763 (2019) 2
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
verdicts
UNVERDICTED 5representative citing papers
A sparse column-wise perturbation plus isotropic Gaussian dither plants a backdoor in CNNs and ViTs that is as hard to detect as Sparse PCA under standard hardness assumptions.
MIST detects Trojaned DNN updates by measuring spectral deviations in pre-activation representations against a benign fine-tuning reference, achieving high accuracy across datasets and attacks after a single update.
An explanation-based detector using seven novel metrics derived from GNN explanations identifies backdoored graphs with high performance on benchmark datasets against multiple attack models.
DeTrigger detects and mitigates backdoor attacks in federated learning via gradient analysis and temperature scaling, claiming up to 251x faster detection and 98.9% attack reduction on four datasets with minimal accuracy loss.
citing papers explorer
-
BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning
BadBone backdoors backbone models with bi-level optimization to make prompt learning on downstream tasks vulnerable while preserving model utility.
-
Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions
A sparse column-wise perturbation plus isotropic Gaussian dither plants a backdoor in CNNs and ViTs that is as hard to detect as Sparse PCA under standard hardness assumptions.
-
Detecting Trojaned DNNs via Spectral Regression Analysis
MIST detects Trojaned DNN updates by measuring spectral deviations in pre-activation representations against a benign fine-tuning reference, achieving high accuracy across datasets and attacks after a single update.
-
Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics
An explanation-based detector using seven novel metrics derived from GNN explanations identifies backdoored graphs with high performance on benchmark datasets against multiple attack models.
-
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning
DeTrigger detects and mitigates backdoor attacks in federated learning via gradient analysis and temperature scaling, claiming up to 251x faster detection and 98.9% attack reduction on four datasets with minimal accuracy loss.