  In this work, we present a learning based method focusing on the
convolutional neural network (CNN) architecture to detect these forgeries. We
consider the detection of both copy-move forgeries and inpainting based
forgeries. For these, we synthesize our own large dataset. In addition to
classification, the focus is also on interpretability of the forgery detection.
As the CNN classification yields the image-level label, it is important to
understand if forged region has indeed contributed to the classification. For
this purpose, we demonstrate using the Grad-CAM heatmap, that in various
correctly classified examples, that the forged region is indeed the region
contributing to the classification. Interestingly, this is also applicable for
small forged regions, as is depicted in our results. Such an analysis can also
help in establishing the reliability of the classification.
