Advancements in machine learning and deep learning techniques have led to the development of sophisticated and accurate face recognition systems. However, for the past few years, researchers are exploring the vulnerabilities of these systems towards digital attacks. Creation of digitally altered images has become an easy task with the availability of various image editing tools and mobile application such as Snapchat. Morphing based digital attacks are used to elude and gain the identity of legitimate users by fooling the deep networks. In this research, partial face tampering attack is proposed, where facial regions are replaced or morphed to generate tampered samples. Face verification experiments performed using two state-of-the-art face recognition systems, VGG-Face and OpenFace on the CMU-MultiPIE dataset indicates the vulnerability of these systems towards the attack. Further, a Partial Face Tampering Detection (PFTD) network is proposed for the detection of the proposed attack. The network captures the inconsistencies among the original and tampered images by combining the raw and high-frequency information of the input images for the detection of tampered images. The proposed network surpasses the performance of the existing baseline deep neural networks for tampered image detection. © 2019 IEEE.