Machine Learning (ML) and Deep Learning (DL) models are increasingly deployed in critical domains such as computer vision, natural language processing (NLP), automatic speech recognition (ASR), time-series analysis, and cybersecurity. Despite their success, these models remain vulnerable to adversarial attacks that can mislead predictions and undermine system reliability. To address this challenge, we conduct a Systematic Literature Review (SLR) of adversarial attacks and defenses in ML/DL, following PRISMA guidelines. Our review analyzes 132 peer-reviewed studies published between 2017 and 2025, identified from leading digital libraries including IEEE, ACM, ScienceDirect, and SpringerLink. The objectives of this study are to: (i) identify state-of-the-art adversarial attack and defense techniques and assess their strengths and limitations; (ii) evaluate how novel threats and countermeasures are addressed across domains; and (iii) propose a unified taxonomy that integrates adversarial attacks, defenses, evaluation metrics, and application domains. Our findings show that adversarial training and certified robustness dominate defense research, but both face trade-offs in scalability and generalizability. Computer vision remains the most studied domain, while NLP, ASR, and time-series research is growing but lacks standardized benchmarks. This review contributes a taxonomy and cross-domain synthesis of adversarial ML research, highlights research gaps, and outlines future directions to advance robustness and support the development of trustworthy AI systems.

His study investigates air pollution prediction in urban Kazakhstan, specifically focusing on Almaty, utilizing machine learning models, LightGBM, and Long Short-Term Memory (LSTM) with an attention mechanism. The research addresses the limitations of current air quality monitoring systems and aims to improve the accuracy of predicting PM2.5 and PM10 concentrations using meteorological data. Results demonstrated that while LightGBM efficiently handled tabular data, LSTM with attention exhibited predictive accuracy by capturing temporal dependencies and handling data variability more effectively. LSTM with attention achieved RMSE values of 5.54 and 5.69 for PM2.5 and PM10, respectively, compared to LightGBM's 4.75 and 5.76. The findings also highlight correlations between pollution levels and environmental conditions such as time of day, wind direction, and temperature. We conclude that LSTM with attention is better suited for air quality predictions in complex urban environments, especially under dynamic meteorological conditions.

Multi-label deep learning models are widely used in real-world applications where predictions depend on the joint presence of several semantically correlated labels. However, existing adversarial attacks largely overlook these inter-label dependencies, often perturbing outputs indiscriminately and producing structurally implausible or easily detectable changes. This paper presents CAPG (Context-Aware Perturbation Generation), a white-box, label-space targeted adversarial framework for generating selective and contextually consistent perturbations in multi-label settings. CAPG incorporates correlation-weighted regularization into the adversarial objective, enabling targeted manipulation of specific labels while preserving the contextual integrity of non-target outputs. Using the Pascal VOC 2012 dataset and a ResNet-101 multi-label classifier, we show that CAPG achieves higher Attack Success Rates (ASR) and substantially improved Contextual Consistency Scores (CCSs) than FGSM, PGD, CW, and DeepFool under identical perturbation budgets. CAPG also produces lower perceptual distortion, yielding adversarial examples that better preserve contextual structure. These results highlight the importance of correlation-aware adversarial evaluation for assessing the robustness of modern multi-label deep learning systems.