arXiv subjects

Qixian Zhang

Publications and source records attributed to Qixian Zhang.

4 recordsLinked to original sources

arXiv2026

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models

While vision and multimodal foundation models underpin critical tasks from perception to complex reasoning, they remain highly vulnerable to adversarial attacks. However, traditional adversarial attacks are typically limited to single, predefined objectives, tightly coupling each attack to a specific model or task, which restricts their scalability and flexibility in real-world scenarios. In this work, we present DarkLLM, a novel attack framework that trains an LLM to translate natural-language attack instructions into latent attack vectors, which are then decoded into visual adversarial perturbations. By leveraging natural-language instruction tuning, DarkLLM not only unifies targeted, untargeted, segmentation, and multi-model attacks within a single framework, but also achieves flexible and controllable adversarial generation, enabling each instruction to produce a perturbation that induces desired behaviors across heterogeneous models. Through extensive experiments across 4 tasks, 13 datasets, and 15 models, we demonstrate that DarkLLM with only 1B parameters can follow attacker instructions and generate highly effective attacks against CLIP, SAM, and frontier LLMs, revealing a systemic vulnerability in modern foundation models.

cs.CR↗

arXiv2025

Perception Activator: An intuitive and portable framework for brain cognitive exploration

Recent advances in brain-vision decoding have driven significant progress, reconstructing with high fidelity perceived visual stimuli from neural activity, e.g., functional magnetic resonance imaging (fMRI), in the human visual cortex. Most existing methods decode the brain signal using a two-level strategy, i.e., pixel-level and semantic-level. However, these methods rely heavily on low-level pixel alignment yet lack sufficient and fine-grained semantic alignment, resulting in obvious reconstruction distortions of multiple semantic objects. To better understand the brain's visual perception patterns and how current decoding models process semantic objects, we have developed an experimental framework that uses fMRI representations as intervention conditions. By injecting these representations into multi-scale image features via cross-attention, we compare both downstream performance and intermediate feature changes on object detection and instance segmentation tasks with and without fMRI information. Our results demonstrate that incorporating fMRI signals enhances the accuracy of downstream detection and segmentation, confirming that fMRI contains rich multi-object semantic cues and coarse spatial localization information-elements that current models have yet to fully exploit or integrate.

cs.CV↗

arXiv2025

Transformer-Based Person Search with High-Frequency Augmentation and Multi-Wave Mixing

The person search task aims to locate a target person within a set of scene images. In recent years, transformer-based models in this field have made some progress. However, they still face three primary challenges: 1) the self-attention mechanism tends to suppress high-frequency components in the features, which severely impacts model performance; 2) the computational cost of transformers is relatively high. To address these issues, we propose a novel High-frequency Augmentation and Multi-Wave mixing (HAMW) method for person search. HAMW is designed to enhance the discriminative feature extraction capabilities of transformers while reducing computational overhead and improving efficiency. Specifically, we develop a three-stage framework that progressively optimizes both detection and re-identification performance. Our model enhances the perception of high-frequency features by learning from augmented inputs containing additional high-frequency components. Furthermore, we replace the self-attention layers in the transformer with a strategy based on multi-level Haar wavelet fusion to capture multi-scale features. This not only lowers the computational complexity but also alleviates the suppression of high-frequency features and enhances the ability to exploit multi-scale information. Extensive experiments demonstrate that HAMW achieves state-of-the-art performance on both the CUHK-SYSU and PRW datasets.

cs.CV↗

arXiv2022

Modeling the impact of tissue oxygen profiles and oxygen depletion parameter uncertainties on biological response and therapeutic benefit of FLASH

FLASH radiation has been reported to efficiently suppress tumor growth while sparing normal tissue, however, the mechanism of the differential tissue sparing effect is still not known. Oxygen has long been known to profoundly impact radiobiological responses, and radiolytic oxygen depletion has been considered to be a possible cause or contributor to the FLASH phenomenon. This work investigates the impact of tissue pO2 profiles, oxygen depletion per unit dose (g), and the oxygen concentration yielding half-maximal radiosensitization (k) in tumor and normal tissue. We developed a model that considers the dependent relationship between oxygen depletion and change of radiosensitivity during FLASH irradiation. Cell survival was calculated based on the LQ-L model and the radiosensitivity related parameters were adjusted while delivering fractional doses of FLASH irradiation The model reproduced published experimental data and was used to analyze the impact of parameter uncertainties on the radiobiological responses. This study expands the oxygen depletion analysis of FLASH to normal human tissue and tumor based on clinically determined aggregate and individual patient pO2 profiles. The results show that the pO2 profile is the most essential factor that affects biological response and analyses based on the median pO2 rather than the full pO2 profile can be unreliable and misleading. Additionally, the presence of a small fraction of cells on the threshold of radiobiologic hypoxia substantially alters biological response to FLASH irradiation. We found that an increment in the k value is generally more protective of tumor than normal tissue due to a higher frequency of lower pO2 values in tumors. Variation in the g value affects the dose at which oxygen depletion impacts response, but does not alter the dose dependent response trends, if the g value is identical in both tumor and normal tissue.

physics.med-ph↗