Research Article
Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges
Dennis Lucky Tuanwi Bale,
Laud Charles Ochei*,
Chidiebere Ugwu
Issue:
Volume 13, Issue 2, April 2024
Pages:
20-28
Received:
5 March 2024
Accepted:
18 March 2024
Published:
2 April 2024
Abstract: The proliferation of deepfake technology poses significant challenges to the integrity and authenticity of visual content in videos, raising concerns about misinformation and deceptive practices. In this paper, we present a comprehensive review of features, techniques, and challenges related to the detection and classification of deepfake images extracted from videos. Existing literature has explored various approaches, including feature-based methods, machine learning algorithms, and deep learning techniques, to mitigate the adverse effects of deepfake content. However, challenges persist, such as the evolution of deepfake generation methods and the scarcity of diverse datasets for training detection models. To address these issues, this paper reviews related work on approaches for deepfake image detection and classification and synthesises these approaches into four categories - feature extraction, machine learning, and deep learning. The findings underscore the importance of continued research efforts in this domain to combat the harmful effects of deepfake technology on society. This study provides recommendations for future research directions, emphasizing the significance of proactive measures in mitigating the spread of manipulated visual content.
Abstract: The proliferation of deepfake technology poses significant challenges to the integrity and authenticity of visual content in videos, raising concerns about misinformation and deceptive practices. In this paper, we present a comprehensive review of features, techniques, and challenges related to the detection and classification of deepfake images ex...
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Research Article
Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges
Bob Chile-Agada,
Laud Charles Ochei*,
Fubara Egbono
Issue:
Volume 13, Issue 2, April 2024
Pages:
29-42
Received:
19 March 2024
Accepted:
3 April 2024
Published:
17 April 2024
Abstract: This paper provides a thorough analysis of the use of electromyography (EMG) data in early stroke diagnosis and detection. Stroke continues to be a major global cause of disability and death, which emphasises the critical need for an accurate diagnosis made quickly to improve patient outcomes. Early detection is still difficult to achieve, even with improvements in medical imaging and testing technologies. By detecting minute variations in muscle activity linked to stroke symptoms, EMG data analysis offers a viable method for early stroke identification. The review delves into the diverse methodologies and strategies utilised to leverage EMG data for the purpose of stroke diagnosis, encompassing the application of deep learning models and machine learning algorithms. The paper proposes a structured framework for classifying approaches for early stroke detection and diagnosis using EMG data, providing a systematic way to categorize and compare different methodologies. The paper concludes by highlighting the revolutionary potential of EMG-based techniques in improving the diagnosis of strokes earlier and urging more study to address current issues and make clinical application easier.
Abstract: This paper provides a thorough analysis of the use of electromyography (EMG) data in early stroke diagnosis and detection. Stroke continues to be a major global cause of disability and death, which emphasises the critical need for an accurate diagnosis made quickly to improve patient outcomes. Early detection is still difficult to achieve, even wit...
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