Detection of Adversarial Attacks on Super-Resolvers Using Spectral Features
The integration of deep learning models into image preprocessing pipelines such as super-resolution introduces a largely unexplored attack vector for adversaries targeting downstream tasks. To ensure trustworthiness of critical imaging pipelines, we must be able to detect adversarial behavior within preprocessing models. In this paper, we propose a spectral-based detection method for identifying adversarial attacks embedded in super-resolution model weights. More specifically, we use the radially-averaged power spectral density as a discriminative feature to train an extreme gradient boosting (XGBoost) detector, demonstrating detectability of model-level threats in super-resolution networ...
| Authors | Emma J. Reid, Haley Duba-Sullivan, Tony G. Allen |
|---|---|
| Primary category | cs.CV |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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