TL;DR
The research methodology involved a systematic literature review using the Scopus database, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and focusing on recent advancements in attack and defence techniques.
This paper advocates for a forward-thinking approach that balances technical sophistication with human-centric principles, ensuring that adversarial deep learning evolves into a discipline not just of technical defense, but also of trust, transparency, and accountability.
Among the evaluated models, CNNs exhibit the highest baseline robustness, whereas DNNs and RNNs rely more heavily on defense mechanisms to maintain performance, whereas DNNs and RNNs rely more heavily on defense mechanisms to maintain performance.
Optimize Deep Learning–based Adversarial Defense Mechanism (ODL-ADM) is proposed in this work, which projects adversarial samples into an immune feature space that is both discriminative and resistant to perturbations.
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.
A systematic framework to enhance adversarial robustness is proposed, validated on the Malimg dataset and supersedes previous approaches by 13.15% in terms of the evasion rate and 37.34% in terms of retraining success.
This review presents a comprehensive analysis of recent deep learning and transfer learning techniques for fake image detection, examining widely adopted convolutional neural network architectures, benchmark datasets, evaluation metrics, and current research developments.
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