A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection
cs.LG
Submitted: 2025-05-19
Updated: 2026-09-01
Comments: Accepted to TMLR 2026
Code: https://github.com/c0510gy/AFLS-AED
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent
- Detecting Adversarial Samples from Artifacts
- Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning
- Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
- On Detecting Adversarial Perturbations
- Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
- Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
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