It is discovered that TabPFN is more robust than all baselines on synthetic data under single-step FGSM attacks for moderate-to-large perturbation budgets, but that this advantage largely disappears under iterative PGD, suggesting that TabPFN’s gradient landscape obstructs single-step attacks.
A robustness-oriented training framework that integrates Mask-Guided Adversarial Mixup (MGAM) and Adaptive Timescale Exponential Moving Average (AT-EMA) that provides a practical data-regularization strategy for improving training stability in adversarial learning is proposed.
Guo Niu, Huanlin Mo, Shengjun Deng et al.· Signal, Image and Video Proc...· 0 citations
Current adversarial defense methods often rely on specific perturbation generation techniques, which face challenges such as limited generalization performance and high computational costs. This paper addresses these issues by examining the response characteristics of intelligent learning models to sensitive adversaria...
Qian Li, Di Wu, Saiyu Qi et al.· International Conference on...· 0 citations
This paper proposes a unified dual-defense framework that jointly integrates adversarial training with a denoising autoencoder (DAE)-based filtering mechanism, specifically designed for imbalanced tabular financial data under adversarial conditions, and explicitly targets adversarial robustness in financial fraud detec...
M. Javeed, Jannatul Maua, M. Mridha et al.· Computers, Materials & C...· 0 citations
The study concluded that adversarial resilience is largely determined by the interaction between model architecture and defense strategy, highlighting the need for architecture-specific defense selection when developing secure medical image classification systems.
Y. Heryadi, I. Sonata, Bambang Krismono Triwijoyo· Matrik· 0 citations
This paper introduces a modified Generative Adversarial Network (MGAN), which generates synthetic images of the minority class (128 × 128 × 3 pixels), thereby enhancing classifier resilience and surpasses conventional data balancing methods in medical imaging contexts.
Roaa Razaq, Ebtesam N. Alshemmary, Zhentai Lu· Iraqi Journal of Science· 0 citations
Deep Neural Networks (DNNs) remain vulnerable to adversarial perturbations, raising significant concerns in image processing applications, particularly in high-stakes domains such as medical imaging and security-critical systems. Most existing defense strategies are limited by domain specificity, architectural dependen...
Syamantak Sarkar, Nirmal Joseph, Sudhish N. George et al.· IEEE Transactions on Image P...· 0 citations
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