Augment and Adapt: A Simple Approach to Image Tampering Detection
Yashas Annadani, C. V. Jawahar · 2018
Convolutional Neural Networks have been shown to be promising for image tampering detection in the recent years. However, the number of tampered images available to train a network is still small. This is mainly due to the cumbersomeness involved in creating lots of tampered images. As a result, the potential offered by these networks is not completely exploited. In this work, we propose a simple method to address this problem by augmenting data using inpainting and compositing schemes. We consider different forms of inpainting like simple inpainting and semantic inpainting as well as compositing schemes like feathering in order to augment the data. A domain adaptation technique is employed to reduce the domain shift between the augmented data and the data available using proprietary softwares. We demonstrate that this method of augmentation is effective in improving the detection accuracies. We present experimental evaluation on two popular datasets for image tampering detection to demonstrate the effectiveness of the proposed approach.