  Artificially crafted images such as memes, seasonal greetings, etc are
flooding the social media platforms today. These eventually start occupying a
lot of internal memory of smartphones and it gets cumbersome for the user to go
through hundreds of images and delete these synthetic images. To address this,
we propose a novel method based on Convolutional Neural Networks (CNNs) for the
on-device filtering of social media images by classifying these synthetic
images and allowing the user to delete them in one go. The custom model uses
depthwise separable convolution layers to achieve low inference time on
smartphones. We have done an extensive evaluation of our model on various
camera image datasets to cover most aspects of images captured by a camera.
Various sorts of synthetic social media images have also been tested. The
proposed solution achieves an accuracy of 98.25% on the Places-365 dataset and
95.81% on the Synthetic image dataset that we have prepared containing 30K
instances.
