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Improving Transferable Targeted Adversarial Attack for Object Detection Using RCEN Framework and Logit Loss Optimization

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Computers, Materials & Continua 2024年第9期80卷 4387-4412页

作者： Zhiyi Ding Lei Sun Xiuqing Mao Leyu Dai Ruiyang Ding School of Cryptography Engineering Information Engineering UniversityZhengzhou450000China

Object detection finds wide application in various sectors,including autonomous driving,industry,and *** studies have highlighted the vulnerability of object detection models built using deep neural networks when confronted with carefully crafted adversarial *** not only reveals their shortcomings in defending against malicious attacks but also raises widespread concerns about the security of existing *** existing adversarial attack strategies focus primarily on image classification problems,failing to fully exploit the unique characteristics of object detectionmodels,thus resulting in widespread deficiencies in their ***,previous research has predominantly concentrated on the transferability issues of non-targeted attacks,whereas enhancing the transferability of targeted adversarial examples presents even greater *** attack techniques typically employ cross-entropy as a loss measure,iteratively adjusting adversarial examples to match target ***,their inherent limitations restrict their broad applicability and transferability across different *** address the aforementioned challenges,this study proposes a novel targeted adversarial attack method aimed at enhancing the transferability of adversarial samples across object detection *** the framework of iterative attacks,we devise a new objective function designed to mitigate consistency issues arising from cumulative noise and to enhance the separation between target and non-target categories(logit margin).Secondly,a data augmentation framework incorporating random erasing and color transformations is introduced into targeted adversarial *** enhances the diversity of gradients,preventing overfitting to white-box ***,perturbations are applied only within the specified object’s bounding box to reduce the perturbation range,enhancing attack *** were conducted on the Microsoft

关键词： Object detection model security targeted attack gradient diversity

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