# On the Adversarial Robustness of Remote Sensing Semantic Change Detection

arXiv: https://arxiv.org/abs/2608.15267 · HTML: https://mlsanity.com/paper/2608.15267/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 15 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [Adversarial Examples for Semantic Segmentation and Object Detection](https://mlsanity.com/paper/1703.08603/) (2017)
- [ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model](https://mlsanity.com/paper/2404.03425/) (2024)
- [Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2108.06103/) (2021)
- [Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2212.05245/) (2022)
- [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://mlsanity.com/paper/2305.05813/) (2023)
- [Physical Adversarial Attacks on an Aerial Imagery Object Detector](https://mlsanity.com/paper/2108.11765/) (2021)
- [Hi-UCD: A Large-scale Dataset for Urban Semantic Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2011.03247/) (2020)
- [Semantic Change Detection with Asymmetric Siamese Networks](https://mlsanity.com/paper/2010.05687/) (2020)
- [Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark](https://mlsanity.com/paper/2202.07054/) (2022)
- [AI Security for Geoscience and Remote Sensing: Challenges and Future Trends](https://mlsanity.com/paper/2212.09360/) (2022)
- [Adversarial Examples in Remote Sensing](https://mlsanity.com/paper/1805.10997/) (2018)
- [Adversarial Example in Remote Sensing Image Recognition](https://mlsanity.com/paper/1910.13222/) (2019)
- [FoBa: A Foreground-Background co-Guided Method and New Benchmark for Remote Sensing Semantic Change Detection](https://mlsanity.com/paper/2509.15788/) (2025)
- [Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models](https://mlsanity.com/paper/2307.16865/) (2023)
- [On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensing](https://mlsanity.com/paper/2501.11462/) (2025)

## Abstract (arXiv)

Semantic change detection (SCD) is a bitemporal dense-prediction task that jointly identifies changed regions and their semantic states before and after change. Unlike single-image segmentation or binary change detection, SCD couples two temporal inputs with timestamp-wise semantic prediction, change localization, and final semantic-change decoding, creating adversarial dependencies that are not captured by conventional robustness protocols. We present a task-specific evaluation framework that separates output-side attack objectives from input-side temporal perturbation access, enabling systematic analysis of component vulnerability and cross-temporal propagation. Experiments on four datasets and six representative CNN-, Transformer-, and state-space-based models evaluate component-level and temporal objectives, single- and dual-timestamp perturbations, multiple attack methods, and cross-architecture transferability. The results show that final semantic-change predictions can be severely corrupted even when binary change localization remains comparatively stable, and that perturbations or attack objectives associated with one timestamp can propagate to the prediction of the other. These behaviors occur across different architecture families, while direct cross-model transfer remains considerably weaker than white-box attacks. The study demonstrates that adversarial robustness in SCD depends on the complete bitemporal prediction pathway rather than on an individual branch or backbone family, and provides a structured protocol for evaluating robustness in coupled bitemporal image analysis. Code is available at https://github.com/EricYu97/AdvSCD.