| Type of Material: | Thesis |
| Title: | An intelligent approach to detect and classify facial image forgery |
| Researcher: | Sheth, Kinjal R |
| Guide: | Vora, Vishal S |
| Department: | Department of Electronics & Communication Engineering |
| Publisher: | Atmiya University |
| Place: | Rajkot |
| Year: | 2023 |
| Language: | English |
| Subject: | Adam | Engineering | Engineering and Technology | Engineering Electrical and Electronic | Fine tuning | Optimizer | Retouching | RMSprop | Transfer Learning | VGG16 | Electronics and Communication Engineering | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Electronics & Communication Engineering, Atmiya University, Rajkot; 2023 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-08 17:22:29 | |
| 008 | __ | 261008t2023||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464024 |
| 040 | __ | |dIN-AhILN|aATMI_360005 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aSheth, Kinjal R |
| 110 | __ | |aDepartment of Electronics & Communication Engineering|0U-0967|dRajkot|bAtmiya University|ein |
| 245 | __ | |aAn intelligent approach to detect and classify facial image forgery |
| 260 | __ | |aRajkot|c2023|bAtmiya University |
| 300 | __ | |aA4|dCD|c76 |
| 502 | __ | |bPhD|d2023|cDepartment of Electronics & Communication Engineering, Atmiya University, Rajkot |
| 518 | __ | |oDate of Award|d2023 |
| 518 | __ | |d2019|oDate of Registration |
| 518 | __ | |d2023|oDate of Completion |
| 520 | __ | |athe era of digital image manipulation and the pervasive presence of social media, the verification of facial image authenticity has become an essential concern. Facial retouching in supporting documents can have adverse effects, undermining the credibility and authenticity of the information presented. In official identification documents, such as passports or driverand#39;s licenses, retouching can hinder accurate identification and security measures, potentially leading to identity fraud and security risks. This study presents a comprehensive investigation into the classification of retouched face images using a fine-tuned pre-trained VGG16 andamp; ResNet50 model with ImageNet weight. We explore the impact of different train-test split strategies on the performance of the model and also evaluate the effectiveness of two distinct optimizers namely Adam and RMSprop. The model generalizability has been checked over two standard datasets ND-IIITD retouched faces and MDRF (Multi Demography Retouched Faces- Cau |
| 650 | __ | |aElectronics and Communication Engineering|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aAdam |
| 653 | __ | |aEngineering |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aEngineering Electrical and Electronic |
| 653 | __ | |aFine tuning |
| 653 | __ | |aOptimizer |
| 653 | __ | |aRetouching |
| 653 | __ | |aRMSprop |
| 653 | __ | |aTransfer Learning |
| 653 | __ | |aVGG16 |
| 700 | __ | |aVora, Vishal S|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/541442|yShodhganga |
| 905 | __ | |afromsg |
User Feedback Comes Under This section.