Deep learning model for deep fake face recognition and detection

ST, Suganthi; Ayoobkhan, Mohamed Uvaze Ahamed; Kumar, K. Vinoth; Bačanin, Nebojša; Venkatachalam, K.; Hubálovský, Štěpán; Trojovský, Pavel

doi:10.7717/peerj-cs.881

Cited by 38 publications

(20 citation statements)

References 22 publications

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“…By using deep learning models such as convolutional neural networks, we have the ability to detect deepfakes from the DeepFakeDetection dataset with very high accuracy, approaching 98.9%. We were able to use both machine learning and deep learning feature extraction methods on periocular data to train an ensemble deep learning model and provide better classification results than previous works, such as [8,11,32].…”

Section: Discussion Conclusion and Future Directionmentioning

confidence: 98%

Using Ensemble Convolutional Neural Network to Detect Deepfakes Using Periocular Data

Johnson¹,

Yuan²,

Roy³

2023

Preprint

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Deepfakes are manipulated or altered images, or video, that are created using deep learning models with high levels of photorealism. The two popular methods of producing a deepfake are based on either convolutional neural networks (CNN), or autoencoders. Deepfakes created using CNN comparatively show higher qualities of realism, yet oftentimes leave artifacts and distortions in the generated media that can be detected using machine learning and deep learning algorithms. In recent years, there has been an influx of periocular image and video data because of the increase usage of face masks. By wearing masks, much of what is used for facial recognition is hidden, leaving only the periocular region visible to an observer. This loss of vital information leads to easier misidentification of media, allowing deepfakes to less likely be identified as fake. In this work, feature extraction methods, such as Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and CNN, are used to train an ensemble deep learning model to detect deepfakes in videos on a frame-by-frame level based on the periocular region. Our proposed model is able to distinguish original and manipulated images with accuracies around 98.9 percent, which is an improvement to previous works by combining SIFT and HOG for deepfake detection in convolutional neural networks.

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Section: Discussion Conclusion and Future Directionmentioning

confidence: 98%

Using Ensemble Convolutional Neural Network to Detect Deepfakes Using Periocular Data

Johnson¹,

Yuan²,

Roy³

2023

Preprint

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“…One of the algorithms that can be used is Local Binary Pattern Histograms (LBPH). In face recognition, LBPH is commonly used to define the face by extracting facial features (ST et al, 2022). LBPH is known to be one of the old face recognition algorithms that requires less computational time and is easy to implement.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning in Face Recognition for Attendance System: An Exploratory Study

Aziz

Ismail

Allias

2022

JCRINN

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Conventional-manual type of attendance systems can be very time-consuming to some extent, particularly for a significant number. The existence of face recognition technology can solve the inefficiency and ineffectiveness of conventional and manual attendance systems. Among many approaches to implement face recognition, this research focuses on using deep learning approaches as it has been proven to give promising results. There are various algorithms for face recognition, such as Local Binary Pattern Histogram (LBPH), Local Binary Pattern Network (LBPn), Haar Cascade, and Convolutional Neural Network. The use of deep learning can reach 98 percent accuracy. However, it is necessary to conduct further research on its implementation on the real system in order to evaluate the efficiency of the system.Â An interview was conducted with an expert in the field, to understand the concept, trend, and use of deep learning in face recognition, as well as to determine the suitable algorithm for the attendance system.Â This paper presents the results from this interview, which provide an insight based on real practices.

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“…The first type is deep learning detection methods, which depend on the CNNs models to classify the DeepFake images. For instance, in the study [2], they used the FisherFace algorithm with the LBP histogram (FF-LBPH) to extract features, then utilized the restricted boltzmann machine (RBM) as a learning rule and the deep belief network (DBN) to detect the DeepFake. Their results show that the LBPH-based feature extraction combined with the deep learning technique helped find the fake face image.…”

Section: Related Workmentioning

confidence: 99%

“…DeepFake uses machine learning (ML) and artificial intelligence (AI) to make deceptive videos and images. Correspondingly, deep learning techniques like autoencoders and generative adversarial networks (GANs) were used to create fake faces that are difficult to expose by humans, even by software [2].…”

Section: Introductionmentioning

confidence: 99%

DeepFake Detection Improvement for Images Based on a Proposed Method for Local Binary Pattern of the Multiple-Channel Color Space

2023

IJIES

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DeepFake is a concern for celebrities and everyone because it is simple to create. DeepFake images, especially high-quality ones, are difficult to detect using people, local descriptors, and current approaches. On the other hand, video manipulation detection is more accessible than an image, which many state-of-the-art systems offer. Moreover, the detection of video manipulation depends entirely on its detection through images. Many worked on DeepFake detection in images, but they had complex mathematical calculations in preprocessing steps, and many limitations, including that the face must be in front, the eyes have to be open, and the mouth should be open with the appearance of teeth, etc. Also, the accuracy of their counterfeit detection in all previous studies was less than what this paper achieved, especially with the benchmark Flickr faces high-quality dataset (FFHQ). This study proposed, a new, simple, but powerful method called image Re-representation by combining the local binary pattern of multiple-channel (IR-CLBP-MC) color space as an image re-representation technique improved DeepFake detection accuracy. The IR-CLBP-MC is produced using the fundamental concept of the multiple-channel of the local binary pattern (MCLBP), an extension of the original LBP. The primary distinction is that in our method, the LBP decimal value is calculated in each local patch channel, merging them to re-represent the image and producing a new image with three color channels. A pretrained convolutional neural network (CNN) was utilized to extract the deep textural features from twelve sets of a dataset of IR-CLBP-MC images made from different color spaces: RGB, XYZ, HLS, HSV, YCbCr, and LAB. Other than that, the experimental results by applying the overlap and non-overlap techniques showed that the first technique was better with the IR-CLBP-MC, and the YCbCr image color space is the most accurate when used with the model and for both datasets. Extensive experimentation is done, and the high accuracy obtained are 99.4% in the FFHQ and 99.8% in the CelebFaces Attributes dataset (Celeb-A).

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Deep learning model for deep fake face recognition and detection

Cited by 38 publications

References 22 publications

Using Ensemble Convolutional Neural Network to Detect Deepfakes Using Periocular Data

Using Ensemble Convolutional Neural Network to Detect Deepfakes Using Periocular Data

Deep Learning in Face Recognition for Attendance System: An Exploratory Study

DeepFake Detection Improvement for Images Based on a Proposed Method for Local Binary Pattern of the Multiple-Channel Color Space

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