From Pixel to Peril: Investigating Adversarial Attacks on Aerial Imagery Through Comprehensive Review and Prospective Trajectories

Kazmi, S Hasan Askari; Aafaq, Nayyer; Khan, Moiz Ahmad; Khalil, Mohsin; Saleem, Ammar

doi:10.1109/access.2023.3299878

Cited by 3 publications

(1 citation statement)

References 139 publications

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“…Kazmi et al [8] delve into the realm of aerial imagery, investigating adversarial attacks on such datasets and proposing prospective trajectories for future research. This is complemented by the work of Nguyen-Vu et al [9], who discuss defensive strategies against spoofing and adversarial attacks, an area crucial for the integrity of biometric systems.…”

Section: Review Of Existing Models For Adversarial Attack Analysismentioning

confidence: 99%

EMAML: Design of an Efficient Ensemble Model for Detection of Adversarial Attacks in Machine Learning Environments

Chetan Patil

2023

IJRITCC

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In the realm of cybersecurity, the escalating sophistication of adversarial attacks poses a significant threat, particularly in the context of machine learning models. Traditional defensive mechanisms often fall short in identifying and mitigating such attacks, primarily due to their static nature and inability to adapt to the evolving strategies of adversaries. This limitation underscores the necessity for more dynamic and responsive approaches. Addressing this critical gap, our research introduces an innovative Active Machine Learning Adversarial Attack Detection framework process. Central to our approach is the strategic amalgamation of data collection and preprocessing techniques. We meticulously gather a diverse dataset encompassing both genuine and adversarial user feedback, which is then carefully annotated to differentiate between the two scenarios. This data undergoes rigorous preprocessing, including tokenization and conversion into numerical features through methods like TF-IDF and word embeddings, paving the way for more nuanced analysis. The core of our model employs a variety of machine learning algorithms—Logistic Regression, Random Forest, SVM, CNN, and XGBoost—each fine-tuned through meticulous hyperparameter optimizations. The novelty of our approach, however, lies in the integration of an active learning strategy for efficient results. By employing uncertainty sampling and query-by-committee, our model actively identifies and learns from instances of highest informational value, continuously evolving in its detection capabilities. Our framework further stands out in its post-training phases. The models are not only retrained with newly labeled data but are also subjected to a comprehensive evaluation on separate test datasets. Metrics such as accuracy, precision, recall, F1-score, and AUC are meticulously computed, ensuring the robustness of our results. Deployed in a real-time environment, the model demonstrates remarkable efficacy in detecting adversarial attacks in user feedback. Continuous monitoring and periodic retraining allow the model to adapt and respond to new adversarial tactics. The impact of our work is quantitatively significant—our model outperforms existing methods with a 9.5% improvement in precision, 8.5% higher accuracy, 8.3% increased recall, 9.4% greater AUC, 4.5% higher specificity, and a 2.9% reduction in detection delays for different scenarios.

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Section: Review Of Existing Models For Adversarial Attack Analysismentioning

confidence: 99%

EMAML: Design of an Efficient Ensemble Model for Detection of Adversarial Attacks in Machine Learning Environments

Chetan Patil

2023

IJRITCC

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The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

Ali,

Zhang

2024

Computers

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This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has revolutionized the field by achieving an optimal balance between speed and accuracy. The review traces the evolution of YOLO variants, highlighting key architectural improvements, performance benchmarks, and applications in domains such as healthcare, autonomous vehicles, and robotics. It also evaluates the framework’s strengths and limitations in practical scenarios, addressing challenges like small object detection, environmental variability, and computational constraints. By synthesizing findings from recent research, this work identifies critical gaps in the literature and outlines future directions to enhance YOLO’s adaptability, robustness, and integration into emerging technologies. This review provides researchers and practitioners with valuable insights to drive innovation in object detection and related applications.

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Adversarial Attacks in Machine Learning: Key Insights and Defense Approaches

Khaleel,

Habeeb,

Alnabulsi

2024

Applied Data Science and Analysis

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There is a considerable threat present in genres such as machine learning due to adversarial attacks which include purposely feeding the system with data that will alter the decision region. These attacks are committed to presenting different data to machine learning models in a way that the model would be wrong in its classification or prediction. The field of study is still relatively young and has to develop strong bodies of scientific research that would eliminate the gaps in the current knowledge. This paper provides the literature review of adversarial attacks and defenses based on the highly cited articles and conference published in the Scopus database. Through the classification and assessment of 128 systematic articles: 80 original papers and 48 review papers till May 15, 2024, this study categorizes and reviews the literature from different domains, such as Graph Neural Networks, Deep Learning Models for IoT Systems, and others. The review posits findings on identified metrics, citation analysis, and contributions from these studies while suggesting the area’s further research and development for adversarial robustness’ and protection mechanisms. The identified objective of this work is to present the basic background of adversarial attacks and defenses, and the need for maintaining the adaptability of machine learning platforms. In this context, the objective is to contribute to building efficient and sustainable protection mechanisms for AI applications in various industries

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From Pixel to Peril: Investigating Adversarial Attacks on Aerial Imagery Through Comprehensive Review and Prospective Trajectories

Cited by 3 publications

References 139 publications

EMAML: Design of an Efficient Ensemble Model for Detection of Adversarial Attacks in Machine Learning Environments

EMAML: Design of an Efficient Ensemble Model for Detection of Adversarial Attacks in Machine Learning Environments

The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

Adversarial Attacks in Machine Learning: Key Insights and Defense Approaches

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