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Hongwei Yu

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3 papers

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AAAI Conference 2025 Conference Paper

A²RNet: Adversarial Attack Resilient Network for Robust Infrared and Visible Image Fusion

Jiawei Li
Hongwei Yu
Jiansheng Chen
Xinlong Ding
Jinlong Wang
Jinyuan Liu
Bochao Zou
Huimin Ma

Infrared and visible image fusion (IVIF) is a crucial technique for enhancing visual performance by integrating unique information from different modalities into one fused image. Exiting methods pay more attention to conducting fusion with undisturbed data, while overlooking the impact of deliberate interference on the effectiveness of fusion results. To investigate the robustness of fusion models, in this paper, we propose a novel adversarial attack resilient network, called A2RNet. Specifically, we develop an adversarial paradigm with an anti-attack loss function to implement adversarial attacks and training. It is constructed based on the intrinsic nature of IVIF and provide a robust foundation for future research advancements. We adopt a Unet as the pipeline with a transformer-based defensive refinement module (DRM) under this paradigm, which guarantees fused image quality in a robust coarse-to-fine manner. Compared to previous works, our method mitigates the adverse effects of adversarial perturbations, consistently maintaining high-fidelity fusion results. Furthermore, the performance of downstream tasks can also be well maintained under adversarial attacks.

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AAAI Conference 2024 Conference Paper

Step Vulnerability Guided Mean Fluctuation Adversarial Attack against Conditional Diffusion Models

Hongwei Yu
Jiansheng Chen
Xinlong Ding
Yudong Zhang
Ting Tang
Huimin Ma

The high-quality generation results of conditional diffusion models have brought about concerns regarding privacy and copyright issues. As a possible technique for preventing the abuse of diffusion models, the adversarial attack against diffusion models has attracted academic attention recently. In this work, utilizing the phenomenon that diffusion models are highly sensitive to the mean value of the input noise, we propose the Mean Fluctuation Attack (MFA) to introduce mean fluctuations by shifting the mean values of the estimated noises during the reverse process. In addition, we reveal that the vulnerability of different reverse steps against adversarial attacks actually varies significantly. By modeling the step vulnerability and using it as guidance to sample the target steps for generating adversarial examples, the effectiveness of adversarial attacks can be substantially enhanced. Extensive experiments show that our algorithm can steadily cause the mean shift of the predicted noises so as to disrupt the entire reverse generation process and degrade the generation results significantly. We also demonstrate that the step vulnerability is intrinsic to the reverse process by verifying its effectiveness in an attack method other than MFA. Code and Supplementary is available at https://github.com/yuhongwei22/MFA

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AAAI Conference 2024 Conference Paper

Transferable Adversarial Attacks for Object Detection Using Object-Aware Significant Feature Distortion

Xinlong Ding
Jiansheng Chen
Hongwei Yu
Yu Shang
Yining Qin
Huimin Ma

Transferable black-box adversarial attacks against classifiers by disturbing the intermediate-layer features have been extensively studied in recent years. However, these methods have not yet achieved satisfactory performances when directly applied to object detectors. This is largely because the features of detectors are fundamentally different from that of the classifiers. In this study, we propose a simple but effective method to improve the transferability of adversarial examples for object detectors by leveraging the properties of spatial consistency and limited equivariance of object detectors’ features. Specifically, we combine a novel loss function and deliberately designed data augmentation to distort the backbone features of object detectors by suppressing significant features corresponding to objects and amplifying the surrounding vicinal features corresponding to object boundaries. As such the target object and background area on the generated adversarial samples are more likely to be confused by other detectors. Extensive experimental results show that our proposed method achieves state-of-the-art black-box transferability for untargeted attacks on various models, including one/two-stage, CNN/Transformer-based, and anchor-free/anchor-based detectors.

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