Sample-wise Targeted Adversarial Attacks on Test-time Adaptation

Test-time adaptation (TTA) effectively counters distribution shifts but exposes models to adversarial manipulation via the unlabeled test stream. Existing class-wise targeted attacks remain impractical 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 the global label distribution of benign queries to evade detection. To achieve this, we propose a meta-learning-based attack with a novel priority-aware gradient alignment strategy that explicitly prioritizes attack success. The strategy formulates the gradient update as an ellipsoidal trust-region problem, mitigating the misalignment between attack success and distributional stealth, while providing theoretical guarantees for effective optimization of the attack objective in the presence of gradient misalignment. Extensive experiments on CIFAR-10-C, CIFAR-100-C, and ImageNet-C across TTA protocols demonstrate that our method achieves high targeted success rates while maintaining a label distribution that is consistent with the no-attack baseline, making it difficult to detect in unlabeled TTA deployment scenarios. Furthermore, we demonstrate that our attack shows strong robustness against existing defenses.

Acousto-optic reconstruction of exterior sound field based on concentric circle sampling with circular harmonic expansion

Acousto-optic sensing provides an alternative approach to traditional microphone arrays by shedding light on the interaction of light with an acoustic field. Sound field reconstruction is a fascinating and advanced technique used in acousto-optics sensing. Current challenges in sound-field reconstruction methods pertain to scenarios in which the sound source is located within the reconstruction area, known as the exterior problem. Existing reconstruction algorithms, primarily designed for interior scenarios, often exhibit suboptimal performance when applied to exterior cases. This paper introduces a novel technique for exterior sound-field reconstruction. The proposed method leverages concentric circle sampling and a two-dimensional exterior sound-field reconstruction approach based on circular harmonic extensions. To evaluate the efficacy of this approach, both numerical simulations and practical experiments are conducted. The results highlight the superior accuracy of the proposed method when compared to conventional reconstruction methods, all while utilizing a minimal amount of measured projection data.