An intelligent approach to detect facial retouching using Fine Tuned VGG16 Online publication date: Thu, 03-Oct-2024
by Kinjal Ravi Sheth
International Journal of Biometrics (IJBM), Vol. 16, No. 6, 2024
Abstract: It is a common practice to digitally edit or 'retouch' facial images for various purposes, such as enhancing one's appearance on social media, matrimonial sites, or even as an authentic proof. When regulations are not strictly enforced, it becomes easy to manipulate digital data, as editing tools are readily available. In this paper, we apply a transfer learning approach by fine-tuning a pre-trained VGG16 model with ImageNet weight to classify the retouched face images of standard ND-IIITD faces dataset. Furthermore, this study places a strong emphasis on the selection of optimisers employed during both the training and fine-tuning stages of the model to achieve quicker convergence and enhanced overall performance. Our work achieves impressive results, with a training accuracy of 99.54% and a validation accuracy of 98.98% for the TL vgg16 and RMSprop optimiser. Moreover, it attains an overall accuracy of 97.92% in the two-class (real and retouching) classification for the ND-IIITD dataset.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biometrics (IJBM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook