FFDL: Feature Fusion-Based Deep Learning Method Utilizing Federated Learning for Forged Face Detection
The widespread adoption of advanced technologies may be responsible for the extensive dissemination of forged photographs and videos on the Internet. This could potentially result in the proliferation of fraudulent identities online, raising safety concerns in society. The traditional method for det...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10816324/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841550770241011712 |
|---|---|
| author | Vinay Gautam Gaganpreet Kaur Meena Malik Ankush Pawar Akansha Singh Krishna Kant Singh S. S. Askar Mohamed Abouhawwash |
| author_facet | Vinay Gautam Gaganpreet Kaur Meena Malik Ankush Pawar Akansha Singh Krishna Kant Singh S. S. Askar Mohamed Abouhawwash |
| author_sort | Vinay Gautam |
| collection | DOAJ |
| description | The widespread adoption of advanced technologies may be responsible for the extensive dissemination of forged photographs and videos on the Internet. This could potentially result in the proliferation of fraudulent identities online, raising safety concerns in society. The traditional method for detecting forgery, commonly referred to as the classical forgery method, lacks the capability to accurately identify such fraudulent activities. This limitation arises because these algorithms are trained on publicly available centralized datasets and do not prioritize privacy and security considerations. Consequently, they adversely affect the ability to detect counterfeit content. As a potential solution to this problem, we employed a highly effective deep learning methodology rooted in federated learning. We introduced a novel deep learning approach that combines features to assess the authenticity of photographs and videos shared on social media platforms. The proposed model was trained using three widely recognized forensic datasets: FaceForensics++, Deepforensic-1.0, and WildDeepfake. Visual features were extracted using two widely recognized deep learning approaches, namelyInception and Xception. These features were then combined into a feature vector using Canonical Correlation Analysis, and Convolutional Neural Networks were trained on these features to identify manipulated images and videos. The experiments were carried out with publicly available datasets and involved changing several parameters. Finally, the proposed model’s performance was compared with other deep learning models within federated learning environments to identify forgeries. Our proposed approach demonstrated exceptional performance, achieving an accuracy rate of 98.99% when evaluated on the merged dataset. |
| format | Article |
| id | doaj-art-69880be2b7ef41cb81bdbb222013947a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-69880be2b7ef41cb81bdbb222013947a2025-01-10T00:01:47ZengIEEEIEEE Access2169-35362025-01-01135366537910.1109/ACCESS.2024.352325710816324FFDL: Feature Fusion-Based Deep Learning Method Utilizing Federated Learning for Forged Face DetectionVinay Gautam0https://orcid.org/0000-0002-0258-5132Gaganpreet Kaur1Meena Malik2https://orcid.org/0000-0001-8654-7143Ankush Pawar3Akansha Singh4https://orcid.org/0000-0002-5520-8066Krishna Kant Singh5https://orcid.org/0000-0002-6510-6768S. S. Askar6https://orcid.org/0000-0002-1167-2430Mohamed Abouhawwash7https://orcid.org/0000-0003-2846-4707Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, IndiaDepartment of CSE, Chandigarh University, Sahibzada Ajit Singh Nagar, IndiaComputer Science and Engineering (AI&ML) Department, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology, Khalapur, IndiaSchool of CSET, Bennett University, Greater Noida, IndiaDelhi Technical Campus, Greater Noida, IndiaDepartment of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi ArabiaDepartment of Animal Science, Michigan State University, East Lansing, MI, USAThe widespread adoption of advanced technologies may be responsible for the extensive dissemination of forged photographs and videos on the Internet. This could potentially result in the proliferation of fraudulent identities online, raising safety concerns in society. The traditional method for detecting forgery, commonly referred to as the classical forgery method, lacks the capability to accurately identify such fraudulent activities. This limitation arises because these algorithms are trained on publicly available centralized datasets and do not prioritize privacy and security considerations. Consequently, they adversely affect the ability to detect counterfeit content. As a potential solution to this problem, we employed a highly effective deep learning methodology rooted in federated learning. We introduced a novel deep learning approach that combines features to assess the authenticity of photographs and videos shared on social media platforms. The proposed model was trained using three widely recognized forensic datasets: FaceForensics++, Deepforensic-1.0, and WildDeepfake. Visual features were extracted using two widely recognized deep learning approaches, namelyInception and Xception. These features were then combined into a feature vector using Canonical Correlation Analysis, and Convolutional Neural Networks were trained on these features to identify manipulated images and videos. The experiments were carried out with publicly available datasets and involved changing several parameters. Finally, the proposed model’s performance was compared with other deep learning models within federated learning environments to identify forgeries. Our proposed approach demonstrated exceptional performance, achieving an accuracy rate of 98.99% when evaluated on the merged dataset.https://ieeexplore.ieee.org/document/10816324/Fake detectiondeep learningfeature fusionfederated learningprivacy-preserving |
| spellingShingle | Vinay Gautam Gaganpreet Kaur Meena Malik Ankush Pawar Akansha Singh Krishna Kant Singh S. S. Askar Mohamed Abouhawwash FFDL: Feature Fusion-Based Deep Learning Method Utilizing Federated Learning for Forged Face Detection IEEE Access Fake detection deep learning feature fusion federated learning privacy-preserving |
| title | FFDL: Feature Fusion-Based Deep Learning Method Utilizing Federated Learning for Forged Face Detection |
| title_full | FFDL: Feature Fusion-Based Deep Learning Method Utilizing Federated Learning for Forged Face Detection |
| title_fullStr | FFDL: Feature Fusion-Based Deep Learning Method Utilizing Federated Learning for Forged Face Detection |
| title_full_unstemmed | FFDL: Feature Fusion-Based Deep Learning Method Utilizing Federated Learning for Forged Face Detection |
| title_short | FFDL: Feature Fusion-Based Deep Learning Method Utilizing Federated Learning for Forged Face Detection |
| title_sort | ffdl feature fusion based deep learning method utilizing federated learning for forged face detection |
| topic | Fake detection deep learning feature fusion federated learning privacy-preserving |
| url | https://ieeexplore.ieee.org/document/10816324/ |
| work_keys_str_mv | AT vinaygautam ffdlfeaturefusionbaseddeeplearningmethodutilizingfederatedlearningforforgedfacedetection AT gaganpreetkaur ffdlfeaturefusionbaseddeeplearningmethodutilizingfederatedlearningforforgedfacedetection AT meenamalik ffdlfeaturefusionbaseddeeplearningmethodutilizingfederatedlearningforforgedfacedetection AT ankushpawar ffdlfeaturefusionbaseddeeplearningmethodutilizingfederatedlearningforforgedfacedetection AT akanshasingh ffdlfeaturefusionbaseddeeplearningmethodutilizingfederatedlearningforforgedfacedetection AT krishnakantsingh ffdlfeaturefusionbaseddeeplearningmethodutilizingfederatedlearningforforgedfacedetection AT ssaskar ffdlfeaturefusionbaseddeeplearningmethodutilizingfederatedlearningforforgedfacedetection AT mohamedabouhawwash ffdlfeaturefusionbaseddeeplearningmethodutilizingfederatedlearningforforgedfacedetection |