Improved Generalizability of Deep-Fakes Detection using Transfer Learning Based CNN Framework

Ranjan, Pranjal; Patil, Sarvesh; Kazi, Faruk

doi:10.1109/icict50521.2020.00021

Cited by 25 publications

(16 citation statements)

References 11 publications

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“…Apart from the research works discussed in Table 5, several researchers have made use of the publicly available benchmark datasets for deepfakes. The authors of [1,10,71,[76][77][78]98] used benchmark datasets shown in Table 5. The benchmark dataset VidTIMIT [115] consists of 35 persons speaking brief words on video and accompanying audio recordings.…”

Section: Discussionmentioning

confidence: 99%

“…FSSPOTTER is used to detect fake faces. A study [77] used deepfake detection (DFD), Celeb-DF, and deepfake detection challenge (DFDC) datasets. For the experimental process, the study used CNN XceptionNet with and without transfer learning.…”

Section: Discussionmentioning

confidence: 99%

“…FSSPOTTER achieves a 91.1% accuracy from UADFV, Celeb-DF 77.6%, Deepfake TIMITHQ 98.5%, and LQ 99.5%, whereas using FaceForensics++, FSSPOTTER obtains 100% accuracy. In [77], the CNN-LSTM combo is used to identify and classify the videos into fake and real. Deepfake detection (DFD), Celeb-DF, and deepfake detection challenge (DFDC) are the datasets utilized in the analysis.…”

Section: Deepfake Video Detection Using Image Processing Techniquesmentioning

confidence: 99%

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A Review of Image Processing Techniques for Deepfakes

Shahzad

Rustam

Flores

et al. 2022

Sensors

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Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complemented by artificial intelligence, deepfakes swap the face of one person with the other and generate hyper-realistic videos. Accompanying the speed of social media, deepfakes can immediately reach millions of people and can be very dangerous to make fake news, hoaxes, and fraud. Besides the well-known movie stars, politicians have been victims of deepfakes in the past, especially US presidents Barak Obama and Donald Trump, however, the public at large can be the target of deepfakes. To overcome the challenge of deepfake identification and mitigate its impact, large efforts have been carried out to devise novel methods to detect face manipulation. This study also discusses how to counter the threats from deepfake technology and alleviate its impact. The outcomes recommend that despite a serious threat to society, business, and political institutions, they can be combated through appropriate policies, regulation, individual actions, training, and education. In addition, the evolution of technology is desired for deepfake identification, content authentication, and deepfake prevention. Different studies have performed deepfake detection using machine learning and deep learning techniques such as support vector machine, random forest, multilayer perceptron, k-nearest neighbors, convolutional neural networks with and without long short-term memory, and other similar models. This study aims to highlight the recent research in deepfake images and video detection, such as deepfake creation, various detection algorithms on self-made datasets, and existing benchmark datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Deepfake Video Detection Using Image Processing Techniquesmentioning

confidence: 99%

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A Review of Image Processing Techniques for Deepfakes

Shahzad

Rustam

Flores

et al. 2022

Sensors

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“…There have been attempts made to overcome the lack of generalizability of these systems by using the concept of transfer learning. As discussed in [113], transfer learning approaches are used where the Xception model is used with pretrained weights of ImageNet. ImageNet has a very good capability of detecting people, animals, birds, etc.…”

Section: A Transfer Learningmentioning

confidence: 99%

Deepfake Generation and Detection: Case Study and Challenges

Patel,

Tanwar,

Gupta

et al. 2023

IEEE Access

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In smart communities, social media has allowed users easy access to multimedia content. With recent advancements in computer vision and natural language processing, machine learning (ML), and deep learning (DL) models have evolved. With advancements in generative adversarial networks (GAN), it has become possible to create fake images/audio/and video streams of a person or use some person's audio and visual details to fit other environments. Thus, deepfakes are specifically used to disseminate fake information and propaganda on social circles that tarnish the reputation of an individual or an organization. Recently, many surveys have focused on generating and detecting deepfake images, audio, and video streams. Existing surveys are mostly aligned toward detecting deepfake contents, but the generation process is not suitably discussed. To address the survey gap, the paper proposes a comprehensive review of deepfake generation and detection and the different ML/DL approaches to synthesize deepfake contents. We discuss a comparative analysis of deepfake models and public datasets present for deepfake detection purposes. We discuss the implementation challenges and future research directions regarding optimized approaches and models. A unique case study, IBMM is discussed, which presents a multi-modal overview of deepfake detection. The proposed survey would benefit researchers, industry, and academia to study deepfake generation and subsequent detection schemes.

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“…Ranjan et al [36] presented a Convolutional Neural Network-based structure and analyzed three public deepfake datasets, i.e., Deepfake Detection Challenge (DFDC) [37], Celeb-DF [38], and DeepfakeDetection (DFD) [39], which is now part of FaceForensics++ [40], as well as a custom high-quality deepfake dataset.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Review of Deep Learning-based Approaches for Deepfake Content Detection

Passos¹,

Jodas²,

Costa³

et al. 2022

Preprint

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The fast-spreading information over the internet is essential to support the rapid supply of numerous public utility services and entertainment to users. Social networks and online media paved the way for modern, timely-communicationfashion and convenient access to all types of information. However, it also provides new chances for ill use of the massive amount of available data, such as spreading fake content to manipulate public opinion. Detection of counterfeit content has raised attention in the last few years for the advances in deepfake generation. The rapid growth of machine learning techniques, particularly deep learning, can predict fake content in several application domains, including fake image and video manipulation. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches. We aim to broaden the state-of-the-art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works and future directions towards the issues and shortcomings still unsolved on deepfake detection.

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Improved Generalizability of Deep-Fakes Detection using Transfer Learning Based CNN Framework

Cited by 25 publications

References 11 publications

A Review of Image Processing Techniques for Deepfakes

A Review of Image Processing Techniques for Deepfakes

Deepfake Generation and Detection: Case Study and Challenges

A Review of Deep Learning-based Approaches for Deepfake Content Detection

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