Deep Fake Detection: Survey of Facial Manipulation Detection Solutions

Pashine, Samay; Mandiya, Sagar; Gupta, Praveen Kumar; Rashid, Sheikh,

doi:10.48550/arxiv.2106.12605

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…An amount of research has indeed been done on it, including a thorough study and implementation of many common algorithms (Albahar & Almalki, 2019;T. T. Nguyen, Nguyen, et al, 2019;Pashine et al, 2021;Saif & Tehseen, 2022;Shelke & Kasana, 2021;Swathi & Sk, 2021;Weerawardana & Fernando, 2021). So, DL has been recognized as an excellent method for detecting synthetic media in the deepfake realm.…”

Section: Relevant Reviewsmentioning

confidence: 99%

Deepfake detection using deep learning methods: A systematic and comprehensive review

Heidari,

Jafari Navimipour,

Dag

et al. 2023

WIREs Data Min & Knowl

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Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human‐level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL‐powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL‐based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, with the accuracy parameter receiving the most attention.This article is categorized under: Technologies > Machine Learning Algorithmic Development > Multimedia Application Areas > Science and Technology

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Section: Relevant Reviewsmentioning

confidence: 99%

Deepfake detection using deep learning methods: A systematic and comprehensive review

Heidari,

Jafari Navimipour,

Dag

et al. 2023

WIREs Data Min & Knowl

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“…Three toolboxes were used in addition to the basic software MATLAB. Applied to uncompressed videos [142] 2021 Mesonets are network structures. Two inception modules and two standard convolution layers were used in Meso-4 and MesoInception-4.…”

Section: Commercial Software Packagesmentioning

confidence: 99%

A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends

El-Shafai,

Fouda,

El-Rabaie

et al. 2023

Multimed Tools Appl

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Thousands of videos are posted on websites and social media every day, including Twitter, Facebook, WhatsApp, Instagram, and YouTube. Newspapers, law enforcement publications, criminal investigations, surveillance systems, Banking, the museum, the military, imaging in medicine, insurance claims, and consumer photography are just a few examples of places where important visual data may be obtained. Thus, the emergence of powerful processing tools that can be easily made available online poses a huge threat to the authenticity of videos. Therefore, it’s vital to distinguish between true and fake data. Digital video forgery detection techniques are used to validate and check the realness of digital video content. Deep learning algorithms lately sparked a lot of interest in the field of digital forensics, such as Recurrent Neural Networks (RNN), Deep Convolutional Neural Networks (DCNN), and Adaptive Neural Networks (ANN). In this paper, we give a soft taxonomy as well as a thorough overview of recent research on multimedia falsification detection systems. First, the basic knowledge needed to comprehend video forgery is provided. Then, a summary of active and passive video manipulation detection approaches is provided. Anti-forensics, compression video methods, datasets required for video forensics, and challenges of video detection approaches are also addressed. Following that, we presented an overview of deepfake, and the datasets required for detection were also provided. Also, helpful software packages and forensics tools for video detection are covered. In addition, this paper provides an overview of video analysis tools that are used in video forensic applications. Finally, we highlight research difficulties as well as interesting research avenues. In short, this survey provides detailed information and a broader investigation to extract data and detect fraud video contents under one umbrella.

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“…Artificial intelligence algorithms have been used in recent years for manipulations that were initially made using image and video editing programs. Deep learning based methods such as automatic encoders and generative adversarial networks are often used to generate fake image and video using artificial intelligence ( Pashine et al, 2021 ). Face image manipulation is the most common type of fake image produced.…”

Section: Introductionmentioning

confidence: 99%

Detection of fake face images using lightweight convolutional neural networks with stacking ensemble learning method

Şafak,

Barışçı

2024

PeerJ Computer Science

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Images and videos containing fake faces are the most common type of digital manipulation. Such content can lead to negative consequences by spreading false information. The use of machine learning algorithms to produce fake face images has made it challenging to distinguish between genuine and fake content. Face manipulations are categorized into four basic groups: entire face synthesis, face identity manipulation (deepfake), facial attribute manipulation and facial expression manipulation. The study utilized lightweight convolutional neural networks to detect fake face images generated by using entire face synthesis and generative adversarial networks. The dataset used in the training process includes 70,000 real images in the FFHQ dataset and 70,000 fake images produced with StyleGAN2 using the FFHQ dataset. 80% of the dataset was used for training and 20% for testing. Initially, the MobileNet, MobileNetV2, EfficientNetB0, and NASNetMobile convolutional neural networks were trained separately for the training process. In the training, the models were pre-trained on ImageNet and reused with transfer learning. As a result of the first trainings EfficientNetB0 algorithm reached the highest accuracy of 93.64%. The EfficientNetB0 algorithm was revised to increase its accuracy rate by adding two dense layers (256 neurons) with ReLU activation, two dropout layers, one flattening layer, one dense layer (128 neurons) with ReLU activation function, and a softmax activation function used for the classification dense layer with two nodes. As a result of this process accuracy rate of 95.48% was achieved with EfficientNetB0 algorithm. Finally, the model that achieved 95.48% accuracy was used to train MobileNet and MobileNetV2 models together using the stacking ensemble learning method, resulting in the highest accuracy rate of 96.44%.

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Deep Fake Detection: Survey of Facial Manipulation Detection Solutions

Cited by 3 publications

References 9 publications

Deepfake detection using deep learning methods: A systematic and comprehensive review

Deepfake detection using deep learning methods: A systematic and comprehensive review

A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends

Detection of fake face images using lightweight convolutional neural networks with stacking ensemble learning method

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