Securing online integrity: a hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness Training
As deepfake technology becomes increasingly sophisticated, the proliferation of manipulated images presents a significant threat to online integrity, requiring advanced detection and mitigation strategies. Addressing this critical challenge, our study introduces a pioneering approach that integrates...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-10-01
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| Series: | Automatika |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2024.2400640 |
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| author | R. Uma Maheshwari B. Paulchamy |
| author_facet | R. Uma Maheshwari B. Paulchamy |
| author_sort | R. Uma Maheshwari |
| collection | DOAJ |
| description | As deepfake technology becomes increasingly sophisticated, the proliferation of manipulated images presents a significant threat to online integrity, requiring advanced detection and mitigation strategies. Addressing this critical challenge, our study introduces a pioneering approach that integrates Explainable AI (XAI) with Adversarial Robustness Training (ART) to enhance the detection and removal of deepfake content. The proposed methodology, termed XAI-ART, begins with the creation of a diverse dataset that includes both authentic and manipulated images, followed by comprehensive preprocessing and augmentation. We then employ Adversarial Robustness Training to fortify the deep learning model against adversarial manipulations. By incorporating Explainable AI techniques, our approach not only improves detection accuracy but also provides transparency in model decision-making, offering clear insights into how deepfake content is identified. Our experimental results underscore the effectiveness of XAI-ART, with the model achieving an impressive accuracy of 97.5% in distinguishing between genuine and manipulated images. The recall rate of 96.8% indicates that our model effectively captures the majority of deepfake instances, while the F1-Score of 97.5% demonstrates a well-balanced performance in precision and recall. Importantly, the model maintains high robustness against adversarial attacks, with a minimal accuracy reduction to 96.7% under perturbations. |
| format | Article |
| id | doaj-art-84cc9372fbb2473cad9009e2cbd3939c |
| institution | Kabale University |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-84cc9372fbb2473cad9009e2cbd3939c2024-11-29T06:50:32ZengTaylor & Francis GroupAutomatika0005-11441848-33802024-10-016541517153210.1080/00051144.2024.2400640Securing online integrity: a hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness TrainingR. Uma Maheshwari0B. Paulchamy1Department of Electronics and Communication Engineering, Hindusthan Institute of Technology, Coimbatore, IndiaDepartment of Electronics and Communication Engineering, Hindusthan Institute of Technology, Coimbatore, IndiaAs deepfake technology becomes increasingly sophisticated, the proliferation of manipulated images presents a significant threat to online integrity, requiring advanced detection and mitigation strategies. Addressing this critical challenge, our study introduces a pioneering approach that integrates Explainable AI (XAI) with Adversarial Robustness Training (ART) to enhance the detection and removal of deepfake content. The proposed methodology, termed XAI-ART, begins with the creation of a diverse dataset that includes both authentic and manipulated images, followed by comprehensive preprocessing and augmentation. We then employ Adversarial Robustness Training to fortify the deep learning model against adversarial manipulations. By incorporating Explainable AI techniques, our approach not only improves detection accuracy but also provides transparency in model decision-making, offering clear insights into how deepfake content is identified. Our experimental results underscore the effectiveness of XAI-ART, with the model achieving an impressive accuracy of 97.5% in distinguishing between genuine and manipulated images. The recall rate of 96.8% indicates that our model effectively captures the majority of deepfake instances, while the F1-Score of 97.5% demonstrates a well-balanced performance in precision and recall. Importantly, the model maintains high robustness against adversarial attacks, with a minimal accuracy reduction to 96.7% under perturbations.https://www.tandfonline.com/doi/10.1080/00051144.2024.2400640Adversarial attacksAdversarial Robustness Training (ART)deepfake technologydetection strategiesdigital content integrityExplainable AI (XAI) |
| spellingShingle | R. Uma Maheshwari B. Paulchamy Securing online integrity: a hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness Training Automatika Adversarial attacks Adversarial Robustness Training (ART) deepfake technology detection strategies digital content integrity Explainable AI (XAI) |
| title | Securing online integrity: a hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness Training |
| title_full | Securing online integrity: a hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness Training |
| title_fullStr | Securing online integrity: a hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness Training |
| title_full_unstemmed | Securing online integrity: a hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness Training |
| title_short | Securing online integrity: a hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness Training |
| title_sort | securing online integrity a hybrid approach to deepfake detection and removal using explainable ai and adversarial robustness training |
| topic | Adversarial attacks Adversarial Robustness Training (ART) deepfake technology detection strategies digital content integrity Explainable AI (XAI) |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2024.2400640 |
| work_keys_str_mv | AT rumamaheshwari securingonlineintegrityahybridapproachtodeepfakedetectionandremovalusingexplainableaiandadversarialrobustnesstraining AT bpaulchamy securingonlineintegrityahybridapproachtodeepfakedetectionandremovalusingexplainableaiandadversarialrobustnesstraining |