Semantic Smoothing via Novel View Synthesis for Robust SAR Image Classification

2026-05-19 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

A novel adversarial defense mechanism called "semantic smoothing" is proposed to enhance the robustness of deep neural networks, particularly for Synthetic Aperture Radar (SAR) automatic target recognition (ATR) systems. Unlike traditional randomized smoothing which uses isotropic noise, semantic smoothing employs a geometry-conditioned novel view synthesis (NVS) model to generate multiple plausible, semantically consistent radar views from a potentially adversarial input. Predictions from these generated views are then aggregated via majority voting to form a robust classifier. Experiments on the MSTAR SAR ATR dataset demonstrate that semantic smoothing significantly improves robustness against both generic attacks like FGSM and PGD, and SAR-specific attacks such as OTSA and SMGAA. The method also enhances clean classification accuracy, indicating its ability to improve generalization by leveraging meaningful transformations.

Key takeaway

For research scientists developing robust SAR ATR systems, semantic smoothing offers a superior alternative to traditional randomized smoothing. You should consider integrating geometry-conditioned novel view synthesis into your defense strategies to generate semantically consistent variations of inputs. This approach not only enhances robustness against diverse adversarial attacks but also improves clean data accuracy, making your models more reliable for safety-critical applications.

Key insights

Semantic smoothing uses generative models to create semantically consistent views for robust classification, outperforming isotropic noise.

Principles

• Preserve semantic structure during adversarial defense.
• Leverage known data generation physics for robustness.

Method

Generate multiple semantically consistent views from an adversarial input using a geometry-conditioned novel view synthesis model, then aggregate classifier predictions via majority voting.

In practice

• Apply NVS to generate varied data for robust models.
• Condition generative models on physical parameters (e.g., azimuth, elevation).

Topics

• Semantic Smoothing
• SAR Image Classification
• Adversarial Robustness
• Novel View Synthesis
• Randomized Smoothing

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.