Deepfake Forensics, an AI-synthesized Detection with Deep Convolutional Generative Adversarial Networks

Gong, Dafeng

doi:10.30534/ijatcse/2020/58932020

Cited by 11 publications

(8 citation statements)

References 45 publications

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“…As a result of the effect of deepfake technology on many areas, research papers in the last two years have become directed towards this technology and explain all the challenges and techniques related to it [27]. Usually, images resulting from the application of deepfake algorithms often need more transformation to fit the area to be forged in the source video.…”

Section: Deepfake Detection 31 Deep Learning Based Methodsmentioning

confidence: 99%

A survey of deepfakes in terms of deep learning and multimedia forensics

Hadi

Kadhem²,

Abbas³

2022

IJECE

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Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfakedetection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a deeper overview of i) the environment of deepfake creation and detection, ii) how deep learning and forensic tools contributed to the detection of deepfakes, and iii) finally how in the future incorporating both deep learning technology and tools for forensics can increase the efficiency of deepfakes detection.

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Section: Deepfake Detection 31 Deep Learning Based Methodsmentioning

confidence: 99%

A survey of deepfakes in terms of deep learning and multimedia forensics

Hadi

Kadhem²,

Abbas³

2022

IJECE

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“…Gong et al [3] conducted an extensive analysis of over a hundred published papers that explore the application of Generative Adversarial Networks (GANs) technology across diverse fields for generating digital multimedia data. The study provides insights into technologies capable of identifying deepfakes, discusses the advantages and risks associated with deepfake technology, and outlines strategies for countering the proliferation of deepfakes.…”

Section: Review Of Literature Much Research Work Has Been Conducted O...mentioning

confidence: 99%

“…Deep learning technology employs multiple layers to encapsulate data abstractions for constructing computational models. Although the training process is time-consuming due to a substantial number of parameters, the testing phase is relatively quick compared to alternative machine learning algorithms.it challenging to discern between genuine and manipulated content [3].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2024

IJTRET

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Deepfake technology, a fusion of deep learning and artificial intelligence, has emerged as a potent tool capable of crafting hyper-realistic yet entirely fabricated multimedia content. This comprehensive review explores the evolution, applications, and underlying principles of deepfake technology, emphasizing its potential implications for privacy, security, and the spread of misinformation. Using advanced deep learning algorithms, particularly Generative Adversarial Networks (GANs), deepfake technology manipulates facial features with remarkable precision, raising concerns about its malicious applications. The review examines the exponential growth in online content sharing facilitated by social media platforms and affordable devices, highlighting the convenience and accessibility but also the risks associated with the widespread use of deepfake technology. The core of deepfake technology, GANs, engages in an iterative competition between a discriminator and a generator, resulting in increasingly convincing synthetic data. A thorough review of the literature reveals significant research efforts focused on deepfake detection, leveraging techniques such as error-level analysis, CNN architectures, and hybrid approaches. The paper discusses regulatory measures, public awareness campaigns, and the critical role of digital forensic evaluation in mitigating deepfake threats. Challenges and concerns, including misinformation, privacy invasion, national security risks, and erosion of trust, are outlined. Mitigation strategies encompass advanced detection algorithms, regulatory frameworks, public awareness, and digital forensic evaluation, emphasizing the collaborative efforts required across technology developers, policymakers, the public, and digital forensic experts to navigate this evolving landscape and safeguard trust, privacy, and security in the digital age.

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“…With the increasing prevalence of deepfake content, it is critical to have reliable and accurate detection systems that can prevent the dissemination of false and misleading information. The proposed approach provides a robust and effective solution to this pressing problem [3].…”

Section: Introductionmentioning

confidence: 99%

Novel Convolutional Neural Networks based Jaya algorithm Approach for Accurate Deepfake Video Detection

Hussain¹,

Ibraheem

2023

MJCS

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Deepfake videos are becoming an increasing concern due to their potential to spread misinformation and cause harm. In this paper, we propose a novel approach for accurately detecting deepfake videos using the combination of Convolutional Neural Networks (CNNs) with the Jaya algorithm optimization. The approach is evaluated on two publicly available datasets, the DeepFake Detection Challenge (DFDC) dataset and the Celeb-DF dataset, and achieves state-of-the-art performance on both datasets. Our approach achieves an accuracy of 99.3% on the DFDC dataset and 97.6% on the Celeb-DF dataset, with high F1 scores indicating a high precision and recall for detecting deepfake videos. Furthermore, our approach is more robust against adversarial attacks than existing state-of-the-art methods. The combination of CNNs with the Jaya algorithm optimization enables effective capture of the temporal information in the video sequence, while the use of robust evaluation metrics ensures objective measurement and comparison with existing methods. Our proposed approach offers a highly effective solution for detecting deepfake videos, which has the potential to be a valuable tool for media forensics, content moderation, and cyber security.

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Deepfake Forensics, an AI-synthesized Detection with Deep Convolutional Generative Adversarial Networks

Cited by 11 publications

References 45 publications

A survey of deepfakes in terms of deep learning and multimedia forensics

A survey of deepfakes in terms of deep learning and multimedia forensics

Untitled

Novel Convolutional Neural Networks based Jaya algorithm Approach for Accurate Deepfake Video Detection

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