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.
Machine learning models, particularly deep learning architectures, achieve high performance in prediction tasks but remain susceptible to adversarial attacks. This study aims to enhance the robustness of Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Recurrent Neural Networks (RNNs), thereby improving the security of machine learning systems. A three-step approach is adopted. First, benign sample classification is performed using the MNIST benchmark dataset. Second, adversarial attacks, namely Projected Gradient Descent (PGD), DeepFool (DF), and the Fast Gradient Sign Method (FGSM), are launched on the trained models, resulting in significant performance degradation. Based on the biased outputs induced by adversarial perturbations, an adversarial detection model is subsequently established. Third, to counteract these attacks, various defense strategies, including adversarial training, defensive distillation, autoencoder-based denoising, ensemble methods, and feature squeezing are employed and evaluated using standard performance metrics and graphical analyses. The results indicate that, in the absence of defense mechanisms, PGD attacks lead to accuracy drops of approximately 27% in CNNs, 83% in DNNs, and 90% in RNNs, demonstrating severe model vulnerabilities. However, when defense strategies are applied, all models recover to an accuracy of at least 98.9%, with adversarial training improving performance under attack by up to 90%. Among the evaluated models, CNNs exhibit the highest baseline robustness, whereas DNNs and RNNs rely more heavily on defense mechanisms to maintain performance. These findings provide valuable insights into the development of secure and resilient machine learning systems capable of mitigating adversarial threats.
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.
The rapid advancement of artificial intelligence (AI) has significantly changed the way digital visual content is created, enabling the generation of highly realistic synthetic images and videos. While these technologies support many beneficial applications, they have also facilitated the creation of manipulated visual content, commonly known as deepfakes, which pose serious challenges to information authenticity, public trust, cybersecurity, and digital forensic investigations. As image manipulation techniques continue to evolve through advanced models such as Generative Adversarial Networks (GANs) and diffusion-based frameworks, conventional detection methods relying on handcrafted features have become increasingly inadequate. In response, deep learning approaches integrated with transfer learning have emerged as effective solutions due to their ability to leverage pre-trained models for extracting robust and discriminative features, even when limited training data are available. This review presents a comprehensive analysis of recent deep learning and transfer learning techniques for fake image detection. It examines widely adopted convolutional neural network (CNN) architectures, benchmark datasets, evaluation metrics, and current research developments. Furthermore, the paper provides a comparative assessment of existing methods by highlighting their strengths, limitations, and performance characteristics. Finally, it identifies major research challenges and outlines future directions for developing robust, scalable, and generalizable fake image detection systems capable of addressing the growing threats posed by AI-generated visual content in cyberspace
