Sparkling Light Publisher Sparklinglight Transactions on Artificial Intelligence and Quantum Computing (STAIQC) Website: https://sparklinglightpublisher.com/ ISSN (Online):2583-0732 E-mail address of authors: a bm783m@att.com, b absa00002@stud.uni-saarland.de © 2022 STAIQC. All rights reserved. Please cite this article as: Biswas Mishra and Abhishek Samanta. (2022). Quantum Transfer Learning for Deepafake Detection. Sparklinglight Transactions on Artificial Intelligence and Quantum Computing (STAIQC), 2(1), 17-27. ISSN (Online):2583-0732. Received Date: 2022/05/02, Reviewed Date: 2022/06/10, Published Date: 2022/06/19. Quantum Transfer Learning Approach for Deepfake Detection Bishwas Mishra a# , Abhishek Samanta b a Senior Software Engineer, AT&T, 4183 Grouse Court #104, Mechanicsburg, PA 17050, USA b Saarland Informatics Campus, Saarland University, Germany Abstract Deepfake image manipulation has achieved great attention in the previous year’s owing to brings solemn challenges from the public self-confidence. Forgery detection in face imaging has made considerable developments in detecting manipulated images. However, there is still a need for an efficient deepfake detection approach in complex background environments. This paper applies the state-of-the-art quantum transfer learning approach for classifying deepfake images from original face images. The proposed model comprises classical pre-trained ResNet-18 and quantum neural network layers that provide efficient features extraction to learn the different patterns of the deepfake face images. The proposed model is validated on a real-world deepfake dataset created using commercial software. An accuracy of 96.1 % was obtained. © 2022 STAIQC. All rights reserved. Keywords: Deepfake, Forgery detection, Quantum Neural Networks. 1. Introduction Rapidly increase in the development of digital image processing field, image modification without visual apparent is becoming easier [1]. These days, seeing does no longer believe. Recently, generative adversarial networks-based imaging forensics attained much attention. Several forensic approaches such as hand-crafted and deep features are commonly utilized for deepfake image classification [3]. Face images are commonly utilized in biological imaging modalities, which comprise intuitive personal information [4]. However, facial images having susceptibility can be forged easily [5]. Recently, significant progress has been made for deepfake image creation using generative networks and computer graphics approaches [6]. These methodologies have been utilized in different facial imaging applications [7]. These approaches can be utilized intentionally for malicious activities [8]. Existing facial imaging approaches might be divided into three steps such as manipulation of the identity, expression, and transfer of the attribute [9]. In the manipulation of the identity process, deepfake images are generated of the entire imaginary person replaced one face with another image of the face through deepfake and the Face Swap [10]. In the manipulation of facial expression, deepfake images are generated with a different expression and also transferred the face expression from the source image to the destination image [11]. Extensive research has been done on deepfake