Fraudulent Face Image Detection

Awhad, Rahul; Jayswal, Saurabh; More, Adesh; Kundale, Jyoti

doi:10.1051/itmconf/20203203005

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…Table 8 below shows the summary of the existing model results and the developed model. Looking at the table above, the developed model outperforms the existing models in [27], [28] and [25]. The developed model was trained with a higher dataset; the Ensemble technique was utilized to make the model more robust which later achieved a higher performance accuracy.…”

Section: Comparative Analysis Of the Proposed Model And The Existing ...mentioning

confidence: 98%

“…In the end, they achieved an accuracy of 62%. In 2020, [27] Developed a model to tackle image deepfakes. The model was built using CNN and SVM.…”

Section: Review Of Machine-learning-related Workmentioning

confidence: 99%

“…The performance of the developed model was compared to existing models in image deepfake detection. The research conducted by [27] used CNN and SVM in the development of their model. Moreso, [28] used CNN in the development of their model.…”

Section: Comparative Analysis Of the Proposed Model And The Existing ...mentioning

confidence: 99%

“…The dataset was further divided into train set, validation set and test set. The dataset used is greater than the one used in the existing model [27] and [28]. Due to limited resources, the research is bonded on the resources available in Kaggle's online development environment.…”

Section: Comparative Analysis Of the Proposed Model And The Existing ...mentioning

confidence: 99%

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Enhancing the Fight against Social Media Misinformation: An Ensemble Deep Learning Framework for Detecting Deepfakes

Aloke,

Abah

2023

IJAIS

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Deepfakes are synthetic media that replace someone's action (source person) with another (target person). Images deepfakes, commonly known as "visual deepfakes," depict a complicated and contentious high-tech avant-garde phenomenon in the sphere of digital trickery and artificial intelligence. These are highly deceitful and computer-based distortions of static images, commonly photographs, where the appearance of a single individual is painstakingly superimposed onto another in a highly sophisticated manner that seems to be real. Image Deepfakes are easy to generate due to easy access to opensource deepfake generation software applications such as FakeApp. Once it is generated, social media becomes its marketplace where it is easily distributed to engage and deceive millions of users. Most research in this area focuses on using a single deep-learning algorithm on a small dataset in the development of the deepfakes detection model. Therefore this research work is focused on building a robust and efficient deepfakes image detection model using a publicly available dataset from Kaggle comprising one hundred and forty thousand (140,000) images. The model was developed using Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Feed Forward Neural Network (FFNN), and Gated Recurrent Unit (GRU). To make the model more robust and efficient, the ensemble technique was employed to ensemble the individual models and an accuracy of 94.91% was achieved.

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Section: Comparative Analysis Of the Proposed Model And The Existing ...mentioning

confidence: 98%

“…In the end, they achieved an accuracy of 62%. In 2020, [27] Developed a model to tackle image deepfakes. The model was built using CNN and SVM.…”

Section: Review Of Machine-learning-related Workmentioning

confidence: 99%