ICaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection

Future Internet

卷:13

SamarSamir Khalil¹  SherinM. Youssef¹  SherineNagy Saleh¹ 

[1] Computer Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt;

关键词: deepfake detection;    capsule network;    CapsNet;    media forensics;    HRNet;    CNN;   

DOI  :  10.3390/fi13040093

来源: DOAJ

【 摘 要 】

Fake media is spreading like wildfire all over the internet as a result of the great advancement in deepfake creation tools and the huge interest researchers and corporations are showing to explore its limits. Now anyone can create manipulated unethical media forensics, defame, humiliate others or even scam them out of their money with a click of a button. In this research a new deepfake detection approach, iCaps-Dfake, is proposed that competes with state-of-the-art techniques of deepfake video detection and addresses their low generalization problem. Two feature extraction methods are combined, texture-based Local Binary Patterns (LBP) and Convolutional Neural Networks (CNN) based modified High-Resolution Network (HRNet), along with an application of capsule neural networks (CapsNets) implementing a concurrent routing technique. Experiments have been conducted on large benchmark datasets to evaluate the performance of the proposed model. Several performance metrics are applied and experimental results are analyzed. The proposed model was primarily trained and tested on the DeepFakeDetectionChallenge-Preview (DFDC-P) dataset then tested on Celeb-DF to examine its generalization capability. Experiments achieved an Area-Under Curve (AUC) score improvement of 20.25% over state-of-the-art models.

【 授权许可】

Unknown