Li Peng^1,2, Xiangbing Li^1,2, Kun Zou¹, Yong Liu^1,2,*, Haibo Huang¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082629 - 23 July 2026

Abstract In recent years, the transferability of adversarial examples has attracted significant attention. To improve the effectiveness of black-box attacks, a frequency-domain decay constraint is introduced, inspired by weight decay and regularization techniques commonly employed during model training. By treating adversarial perturbations as inputs in an optimization process, this constraint aims to mitigate the excessive reliance on low-frequency components during adversarial example generation, thereby enhancing transferability. Fourier heatmaps are utilized to analyze the sensitivity of input samples, enabling a decomposition of the frequency spectrum into low-frequency and high-frequency components. Based on this analysis, low-frequency attenuation is More >

Zhichao Pei, Ou Ye^*, Panyu Yang, Kaiwen He

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081490 - 15 June 2026

Abstract To address the issue of insufficient transferability of existing adversarial example generation methods for vision-language pre-training (VLP) models, this paper proposes an adversarial example transfer method for VLP models based on negative sample feature perturbation. First, a novel cross-modal collaborative perturbation strategy is constructed. By introducing negative samples into the cross-modal perturbation mechanism, the strategy explores more perturbation directions, breaks the original modal alignment constraints and avoids the local focus of adversarial perturbations. Then, to reduce the computational cost, a dynamic threshold attack strategy is built to measure the modal similarity of the generated adversarial… More >

Sudhir Kumar Pandey¹, Jian-Xun Mi^1,*, Zahid Ullah², Mona Jamjoom³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074811 - 09 April 2026

Abstract Adversarial examples in object detection frequently fail to transfer between different models because attacks overfit to the source model’s architecture and feature space. We propose AugTrans, a framework that addresses this limitation through input-space regularization. Our key innovation is a multi-stage augmentation pipeline that incorporates object-level semantic awareness into transformation design. The pipeline comprises three novel components: dynamic object-centric rotation with adaptive scheduling, multi-box aware resizing based on ground-truth annotations, and composite noise injection. These transformations are integrated within the Expectation over Transformation (EOT) framework. By optimizing perturbations to remain effective across semantically meaningful transformations, our… More >

Haolang Feng^1,2, Yuling Chen^1,2,*, Yang Huang^1,2, Xuewei Wang³, Haiwei Sang⁴

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4469-4490, 2025, DOI:10.32604/cmc.2025.064896 - 30 July 2025

Abstract Deep neural networks remain susceptible to adversarial examples, where the goal of an adversarial attack is to introduce small perturbations to the original examples in order to confuse the model without being easily detected. Although many adversarial attack methods produce adversarial examples that have achieved great results in the white-box setting, they exhibit low transferability in the black-box setting. In order to improve the transferability along the baseline of the gradient-based attack technique, we present a novel Stochastic Gradient Accumulation Momentum Iterative Attack (SAMI-FGSM) in this study. In particular, during each iteration, the gradient information More >

Xiaoyin Yi^1,2, Long Chen^1,3,4,*, Jiacheng Huang¹, Ning Yu¹, Qian Huang⁵

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 157-175, 2025, DOI:10.32604/cmc.2025.059863 - 26 March 2025

Abstract Transfer-based Adversarial Attacks (TAAs) can deceive a victim model even without prior knowledge. This is achieved by leveraging the property of adversarial examples. That is, when generated from a surrogate model, they retain their features if applied to other models due to their good transferability. However, adversarial examples often exhibit overfitting, as they are tailored to exploit the particular architecture and feature representation of source models. Consequently, when attempting black-box transfer attacks on different target models, their effectiveness is decreased. To solve this problem, this study proposes an approach based on a Regularized Constrained Feature More >

Chengsheng Yuan^1,2, Baojie Cui^1,2, Zhili Zhou³, Xinting Li^4,*, Qingming Jonathan Wu⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 899-914, 2024, DOI:10.32604/cmc.2023.045854 - 30 January 2024

Abstract In recent years, deep learning has been the mainstream technology for fingerprint liveness detection (FLD) tasks because of its remarkable performance. However, recent studies have shown that these deep fake fingerprint detection (DFFD) models are not resistant to attacks by adversarial examples, which are generated by the introduction of subtle perturbations in the fingerprint image, allowing the model to make fake judgments. Most of the existing adversarial example generation methods are based on gradient optimization, which is easy to fall into local optimal, resulting in poor transferability of adversarial attacks. In addition, the perturbation added… More >

Bin Lin¹, Fei Gao², Wenli Zeng^3,*, Jixin Chen⁴, Cong Zhang⁵, Qinsheng Zhu⁶, Yong Zhou⁴, Desheng Zheng⁴, Qian Qiu^7,5, Shan Yang⁸

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3075-3085, 2023, DOI:10.32604/csse.2023.034268 - 03 April 2023

Abstract The current adversarial attacks against deep learning models have achieved incredible success in the white-box scenario. However, they often exhibit weak transferability in the black-box scenario, especially when attacking those with defense mechanisms. In this work, we propose a new transfer-based black-box attack called the channel decomposition attack method (CDAM). It can attack multiple black-box models by enhancing the transferability of the adversarial examples. On the one hand, it tunes the gradient and stabilizes the update direction by decomposing the channels of the input example and calculating the aggregate gradient. On the other hand, it More >