Efficient-Frequency: a hybrid visual forensic framework for facial forgery detection

Du, Chau Xuan Truong; Duong, Le Hoang; Huynh, Thanh Trung; Tam, Pham Minh; Nguyen, Quoc Viet Hung; Jo, Jun

doi:10.1109/ssci47803.2020.9308305

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…The researchers in [96] proposed patch and pair convolutional neural networks (PPCNN). In [97], authors performed an analysis in the frequency domain by exploiting the image latent patterns' richness. A modern approach called ID-revelation [98] was proposed to learn temporal facial features based on a person's movement during talking.…”

Section: ) Deep Learning Based Methodsmentioning

confidence: 99%

Deepfake Detection: A Systematic Literature Review

et al. 2022

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Over the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia. Though the technology has been mostly used in legitimate applications such as for entertainment and education, etc., malicious users have also exploited them for unlawful or nefarious purposes. For example, high-quality and realistic fake videos, images, or audios have been created to spread misinformation and propaganda, foment political discord and hate, or even harass and blackmail people. The manipulated, high-quality and realistic videos have become known recently as Deepfake. Various approaches have since been described in the literature to deal with the problems raised by Deepfake. To provide an updated overview of the research works in Deepfake detection, we conduct a systematic literature review (SLR) in this paper, summarizing 112 relevant articles from 2018 to 2020 that presented a variety of methodologies. We analyze them by grouping them into four different categories: deep learning-based techniques, classical machine learning-based methods, statistical techniques, and blockchain-based techniques. We also evaluate the performance of the detection capability of the various methods with respect to different datasets and conclude that the deep learning-based methods outperform other methods in Deepfake detection.

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Section: ) Deep Learning Based Methodsmentioning

confidence: 99%

Deepfake Detection: A Systematic Literature Review

et al. 2022

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“…Effective Deepfake image classification has been proposed using a novel feature extraction method. 43 In 44 , several XeptionNet models are combined with a Deepfake detection technique to identify the differences between faces and their context.…”

Section: Figure 1: Various Approaches Used For Deepfake Detectionmentioning

confidence: 99%

GenConVit+: Advanced hybrid framework for deepfake detection for safeguarding digital media integrity

Patil,

Sangolgi,

Bag

et al. 2024

JIST

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The propagation of deepfake videos has introduced serious concerns, particularly in their potential to Circulate misleading details and undermine the integrity of digital media. In response to this challenge, we present the Generative Convolutional Vision Transformer (GenConVit+) as a robust solution for deepfake video detection. GenConVit+ integrates the strengths of ConvNeXt and Swin Transformer models with a 3D Convolutional neural network (CNN) to extract relevant features. It further harnesses the capabilities of Autoencoders and Variational Autoencoders to discern patterns in latent data distribution. Our model's proficiency is validated through rigorous training and evaluation on four distinct datasets. DFDC, FF++, DeepFakeTIMIT, and Celeb-DF (v2). The results speak volumes, with GenConVit+ achieving notably high classification accuracy, F1 Scores, and AUC values. It rises to the challenge of generalizability in deepfake detection by effectively differentiating a wide spectrum of falsified videos while upholding the integrity of digital media. On average, the GenConVit+ model attains an accuracy of 95.6% and an impressive AUC value of 99.3% across the datasets we examined. This underscores its capacity to robustly detect deepfake content and maintain the integrity of digital media.

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“…Early face detection with enhanced accuracy ensures that early face detection with approval of various applications of face detection in the form of the whole networking system, which enables the early identification of applications that are intended to be fraudulent. The forgery function in the form of enhanced accuracy that the way function of the detection in the accuracy classification form has been impacted in recent years, and this paper proposes that the novel end-to-end visual forensic delay applications are provided in the form of two separate efficient Nets, which can be used to detect facial forgeries [14]. The forgery function in the form of enhanced accuracy that the way function of the detection in the accuracy classification form has been impacted in recent years.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Fake Faces with AI: A Deep Neural Network Improvement Project

Kumar¹,

Yadav²,

S.Yuvaraj³

2023

IJSMEM

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Forgeries created with deep face techniques have become increasingly common in several fields in recent years, including politics, education, and the democratic process, and as a result, many scholars are working on strategies to detect and prevent such forgeries. Since these programmes typically employ machine learning or fuzzy logic, accurate data classification is not something they can promise. However, it is common knowledge that forgery detection methods necessitate a shared, massive dataset, and that facial recognition systems benefit most from deep learning’s precision. Our suggested approaches make use of images and videos from the VGG-19 shared dataset, with genetic algorithm-based feature extraction and an improved convolutional neural network handling classifications for the trained datasets, respectively. A gaussian filter is used as preliminary processing on the VGG-19 common dataset.

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Efficient-Frequency: a hybrid visual forensic framework for facial forgery detection

Cited by 13 publications

References 18 publications

Deepfake Detection: A Systematic Literature Review

Deepfake Detection: A Systematic Literature Review

GenConVit+: Advanced hybrid framework for deepfake detection for safeguarding digital media integrity

Detecting Fake Faces with AI: A Deep Neural Network Improvement Project

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