# The Double-Edged Sword of Data-Driven Super-Resolution: Adversarial Super-Resolution Models

arXiv: https://arxiv.org/abs/2602.07251 · HTML: https://mlsanity.com/paper/2602.07251/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery](https://mlsanity.com/paper/1812.04098/) (2018)
- [Task-Driven Super Resolution: Object Detection in Low-resolution Images](https://mlsanity.com/paper/1803.11316/) (2018)
- [Backdoor Attacks against Image-to-Image Networks](https://mlsanity.com/paper/2407.10445/) (2024)
- [BadRefSR: Backdoor Attacks Against Reference-based Image Super Resolution](https://mlsanity.com/paper/2502.20943/) (2025)
- [Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution](https://mlsanity.com/paper/2503.04385/) (2025)
- [2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts](https://mlsanity.com/paper/2412.04525/) (2024)
- [BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution](https://mlsanity.com/paper/2505.15308/) (2025)

## Built on by (most cited first, 1 of 1)

- [Detection of Adversarial Attacks on Super-Resolvers Using Spectral Features](https://mlsanity.com/paper/2609.35022/) (2026)

## Abstract (arXiv)

Data-driven super-resolution (SR) methods are often integrated into imaging pipelines as preprocessing steps to improve downstream tasks such as classification and detection. However, these SR models introduce a previously unexplored attack surface into imaging pipelines. In this paper, we present AdvSR, a framework demonstrating that adversarial behavior can be embedded directly into SR model weights during training, requiring no access to inputs at inference time. Unlike prior attacks that perturb inputs or rely on backdoor triggers, AdvSR operates entirely at the model level. By jointly optimizing for reconstruction quality and targeted adversarial outcomes, AdvSR produces models that appear benign under standard image quality metrics while inducing downstream misclassification. We evaluate AdvSR on three SR architectures (SRCNN, EDSR, SwinIR) paired with a YOLOv11 classifier and demonstrate that AdvSR models can achieve high attack success rates with minimal quality degradation. These findings highlight a new model-level threat for imaging pipelines, with implications for how practitioners source and validate models in safety-critical applications.