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deep fake Detection Using Computer Vision-Based deep Neural Network with Pairwise Learning

Intelligent Automation & Soft Computing 2023年第2期35卷 2449-2462页

作者： R.Saravana Ram M.Vinoth Kumar Tareq M.Al-shami Mehedi Masud Hanan Aljuaid Mohamed Abouhawwash Department of Electronics and Communication Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Mathematics Faculty of ScienceSana’a UniversitySana’a 13509Yemen Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

deep learning-based approaches are applied successfully in manyﬁelds such as deepfake identiﬁcation,big data analysis,voice recognition,and image *** is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artiﬁcial intelligence for political abuse,spreading false information,and *** artiﬁcial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and *** paper has analyzed the features related to the computer vision of digital content to determine its *** method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction *** the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning *** proposed approach has improved the accuracy of the detection rate by 98%on various datasets.

关键词： deep fake deep belief network fuzzy clustering feature extraction pairwise learning

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Reducing Dataset Specificity for deepfakes Using Ensemble Learning

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Computers, Materials & Continua 2023年第2期74卷 4261-4276页

作者： Qaiser Abbas Turki Alghamdi Yazed Alsaawy Tahir Alyas Ali Alzahrani Khawar Iqbal Malik Saira Bibi Faculty of Computer and Information Systems Islamic University Madinah Madinah42351Saudi Arabia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan The University of Lahore Sargodha40100Pakistan University of Sargodha Department of Computer Science&ITSargodha40100Pakistan

The emergence of deep fake videos in recent years has made image falsification a real danger.A person’s face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional *** of how clever these videos are frequently,Manipulation is challenging to *** media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a *** earlier times,it was not so easy to alter the videos,which required expertise in the domain and ***,the generation of fake videos has become easier and with a high level of realism in the *** are forgeries and altered visual data that appear in still photos or video *** automatic identification systems have been developed to solve this issue,however they are constrained to certain datasets and performpoorly when applied to different *** study aims to develop an ensemble learning model utilizing a convolutional neural network(CNN)to handle deepfakes or *** employed ensemble learning,a technique combining many classifiers to achieve higher prediction performance than a single classifier,boosting themodel’s *** performance of the generated model is evaluated on Face *** work is about building a new powerful model for automatically identifying deep fake videos with the deepfake-Detection-Challenges(DFDC)*** test our model using the DFDC,one of the most difficult datasets and get an accuracy of 96%.

关键词： deep machine learning deep fake CNN DFDC ensemble learning

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