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  • Open Access

    ARTICLE

    Multi-Branch Deepfake Detection Algorithm Based on Fine-Grained Features

    Wenkai Qin1, Tianliang Lu1,*, Lu Zhang2, Shufan Peng1, Da Wan1

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 467-490, 2023, DOI:10.32604/cmc.2023.042417

    Abstract With the rapid development of deepfake technology, the authenticity of various types of fake synthetic content is increasing rapidly, which brings potential security threats to people's daily life and social stability. Currently, most algorithms define deepfake detection as a binary classification problem, i.e., global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false. However, the differences between real and fake samples are often subtle and local, and such global feature-based detection algorithms are not optimal in efficiency and accuracy. To this end, to enhance the extraction of forgery details… More >

  • Open Access

    ARTICLE

    Deepfake Video Detection Based on Improved CapsNet and Temporal–Spatial Features

    Tianliang Lu*, Yuxuan Bao, Lanting Li

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 715-740, 2023, DOI:10.32604/cmc.2023.034963

    Abstract Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms, presenting risks for numerous countries, societies, and individuals, and posing a serious threat to cyberspace security. To address the problem of insufficient extraction of spatial features and the fact that temporal features are not considered in the deepfake video detection, we propose a detection method based on improved CapsNet and temporal–spatial features (iCapsNet–TSF). First, the dynamic routing algorithm of CapsNet is improved using weight initialization and updating. Then, the optical flow algorithm is used to extract interframe temporal features of the videos to… More >

  • Open Access

    ARTICLE

    Reducing Dataset Specificity for Deepfakes Using Ensemble Learning

    Qaiser Abbas1, Turki Alghamdi1, Yazed Alsaawy1, Tahir Alyas2,*, Ali Alzahrani1, Khawar Iqbal Malik3, Saira Bibi4

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4261-4276, 2023, DOI:10.32604/cmc.2023.034482

    Abstract 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 content. Because of how clever these videos are frequently, Manipulation is challenging to spot. Social media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a human. In earlier times, it was not so easy to alter the videos, which required expertise in the domain… More >

  • Open Access

    ARTICLE

    Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods

    Wahidul Hasan Abir1, Faria Rahman Khanam1, Kazi Nabiul Alam1, Myriam Hadjouni2, Hela Elmannai3, Sami Bourouis4, Rajesh Dey5, Mohammad Monirujjaman Khan1,*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2151-2169, 2023, DOI:10.32604/iasc.2023.029653

    Abstract Nowadays, deepfake is wreaking havoc on society. Deepfake content is created with the help of artificial intelligence and machine learning to replace one person’s likeness with another person in pictures or recorded videos. Although visual media manipulations are not new, the introduction of deepfakes has marked a breakthrough in creating fake media and information. These manipulated pictures and videos will undoubtedly have an enormous societal impact. Deepfake uses the latest technology like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to construct automated methods for creating fake content that is becoming increasingly difficult to detect with the human… More >

  • Open Access

    ARTICLE

    DeepFake Videos Detection Based on Texture Features

    Bozhi Xu1, Jiarui Liu1, Jifan Liang1, Wei Lu1,*, Yue Zhang2

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1375-1388, 2021, DOI:10.32604/cmc.2021.016760

    Abstract In recent years, with the rapid development of deep learning technologies, some neural network models have been applied to generate fake media. DeepFakes, a deep learning based forgery technology, can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes. The spread of face manipulation videos is very easy to bring fake information. Therefore, it is important to develop effective detection methods to verify the authenticity of the videos. Due to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in… More >

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