Sample-wise Targeted Adversarial Attacks on Test-time Adaptation
Test-time adaptation (TTA) mitigates distribution shifts by adapting models to unlabeled test inputs, but also exposes them to adversarial manipulation. Existing class-wise targeted attacks remain suboptimal for stealthy exploitation in this setting: since TTA operates on batches, forcing a subset of samples toward a target label unintentionally pulls similar benign samples along, resulting in a conspicuously high frequency of the target label that is easy to detect. To capture a more realistic threat, we introduce a sample-wise targeted attack. Unlike prior approaches, the attacker aims to misclassify only inputs carrying an attacker-chosen trigger, while preserving a benign-like predict...
| Authors | Phuc Duc Nguyen, Quang Duc Nguyen |
|---|---|
| Primary category | cs.LG |
| Published | 22 May 2026 |
| Last updated at source | 4 October 2026 |
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