Encoder-decoder-based CNN model for detection of object removal by image inpainting

In multimedia forensics, several methods have been developed for the authentication of digital images. However, the detection and localization of removed objects from an image has always been a challenging problem. Image forgery, for the removal of objects, can be done in many ways. Among them, image inpainting performs object removal and fills the empty region with surrounding patches. The clues of inpainted region are visually imperceptible. Till date, limited work has been done for image inpainting detection. Hence, a convolutional neural network-based model for the detection of inpainted regions in an image is presented in this research. A hybrid encoder-decoder-based architecture is proposed, where a segment of DenseNet-121 architecture is adopted as an encoder. The primary goal of this architecture is to use spatial maps to explore the distinguishing features between inpainted and uninpainted regions. Inpainted image dataset created by using the exemplar-based image inpainting method is used to train and validate the proposed model. The performance of the proposed model is evaluated using various performance metrics. Experimental results show that the proposed model outperformed existing methods for a variety of inpainted images. (c) 2023 SPIE and IS&T