On-device Filtering of Social Media Images for Efficient Storage
This addresses storage inefficiency for smartphone users by enabling efficient deletion of synthetic social media images, though it is incremental as it builds on existing CNN techniques.
The paper tackles the problem of synthetic images like memes and greetings occupying smartphone storage by proposing an on-device CNN-based filtering method, achieving 98.25% accuracy on the Places-365 dataset and 95.81% on a custom synthetic image dataset.
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.