Quantum Error Correction (QEC) is essential for future quantum computers due to its ability to exponentially suppress physical errors. The surface code is a leading error-correcting code candidate because of its local topological structure, experimentally achievable thresholds, and support for universal gate operations with magic states. However, its physical overhead scales quadratically with number of correctable errors. Conversely, quantum low-density parity-check (qLDPC) codes offer superior scaling but lack, on their own, a clear path to universal logical computation. Therefore, it is becoming increasingly evident that there are significant advantages to designing architectures using multiple codes. Heterogeneous architectures provide a clear path to universal logical computation as well as the ability to access different resource trade offs.

Remarkable advancements have been made in the field of object detection, and given its widespread application, it is of paramount importance to investigate the robustness of detection models. However, previous methods have primarily focused on models based on Convolutional Neural Networks (CNNs), seriously neglecting the Transformer-based models that develop rapidly but exhibit obvious differences in terms of information processing. Therefore, this paper aims to address this gap by exploring potential attacks arising from the self-attention mechanism inhered in Transformer. Specifically, we propose a novel adversarial attack scenario targeting Transformer-based object detection models, where only objects of specific class fail to be detected, while irrelevant objects remain undisturbed. Therefore, human perception is hard to find errors even with the detector fail. To achieve this goal, we introduce an adversarial patch generation method, termed Class-Specific Adversarial (CSAdv) patches, which simultaneously leverages class probability to attack specific objects and utilizes the output from Transformer decoder structures, Query Output, to protect irrelevant objects. Due to the long-range interactions of Transformer, the adversarial patch does not need to directly cover or closely surround the specific objects. Instead, it achieves remote targeted attacks simply by being placed in the corner of image, which greatly enhances the concealment of patches. Extensive experiments are conducted on various benchmark datasets and Transformer-based baselines, and the experimental results show that CSAdv can effectively mask certain class while keeping other classes as unaffected as far as possible.