Deep learning model for deep fake face recognition and detection

Introduction

Digital manipulation of the face images include facial information of fake images using deep fake approaches (Korshunov & Marcel, 2018). In recent years, deepfake approach has become a popular technique in detecting fake images recently (Citron, 2019; Cellan-Jones, 2019). It is implemented by using deep learning technique in order to create fake images by swapping the face of one person by the face of another. In the year 2017, it was termed as Reddit user and it is commonly known as “deep fakes” using deep adversarial models (i.e.) Generative Adversarial Networks (GAN) and similarly, it transform the celebrity faces into porn videos (Maras & Alexandrou, 2019). The main issues in the fake pornography are including fake news in the content, financial fraud and hoaxes. At the same time, the advantages of deep fake is in the fields such as virtual reality, editing film and production. The chief working concepts of deep fakes are merging, replacing, combining and superimposing images using deep learning and machine learning techniques so as to create fake digital images or videos (Maras & Alexandrou, 2019).

Many software apps/tools are available through which deep fake images are created without a programming knowledge and technical side background information. Usually the profile pictures from the social media are taken and fake images or videos are developed with a help of the expert. Security enhancement in the detection of face swap and the accuracy are very low. To overcome these issues, this paper proposes a new strategy for detecting the deep fake facial images using the fisher face with an LBPH approach. In the digital image manipulation, techniques are applied in many fields which give more misinformation in the society. The scenarios such as creating fake news, providing false information in the political elections, create security threats (Allcott & Gentzkow, 2017; Lazer et al., 2018).

CNN based methods like XceptionNet, Meso Inception-Net, ResNet are used in the field of detecting deep fakes which include detection of visual artifacts, inconsistency in color during the time of performing blend operations in the image analysis. The process include identifying the forgery of face X-ray by blending the boundary forged image in the CNN model and classifying the loss in the detection of face X-ray (Li et al., 2020; Li & Lyu, 2018; Afchar et al., 2018; Dang et al., 2020). In the media, articles use the biometric technology for the detection of deep fakes. In order to detect the difference between real and fake images in the field of ocular biometric, CNN methods such as Squeeze Net, DenseNet, ResNet and light CNN are used  (Nguyen & Derakhshani, 2020). The main contributions of this research work are the following:

1. A new hybrid high-performance deep fake face detection method is used based on the analysis of the Fisher face algorithm (LBHH) with dimensional reduction in features of the face image.

2. To detect the fake and real image using deepfake detection classifier based on DBN with the RBM technique.

The paper has been organized as follows: section 2 describes about the review of literature, section 3 introduces deep fake detection using FF-LBPH-DBN, section 4 explains about the experimental results and section 5 concludes the paper with future directions.

Related works

Deep fakes in the face manipulation are the frightening thing to distort the original facts in the digital images. In the advancement of technologies, deep fake detection of algorithms are necessary to be used these days in verifying the content of digital manipulation information. By using deep learning algorithms such as Generative Adversarial Neural Networks (GANs) are based on the concept of auto encoders and decoders for the implementation of detecting the fake images or videos (Yadav & Salmani, 2019). Deep fakes which include swapping of face images are carried out without the knowledge of the celebrities. It is also used to misrepresent the face images of the politicians. At first, swapping of face image was done in the photo of Abraham Lincoln (Badale et al., 2018). Yang, Li & Lyu (2019) have proposed a model to detect deep fake using head poses inconsistency. By using that model, the faces for various persons were created without modifying the original face expressions. Jagdale & Shah (2019) paper proposed an algorithm of NA-VSR for super resolution. The concept of the algorithm is that it reads the video and converts into frame by frame (Maheswaran et al., 2017). Then, the median filter is applied to remove the unnecessary noise in the video. By the use of bicubic interpolation technique, the density of the pixel in the image gets increased. Bicubic transformation is applied for the enhancement of the image. Yadav & Salmani (2019) have described the working principle of the deep fake techniques along with swapping of face images in a high precision value (Maheswaran et al., 2018). Generative Adversarial Neural Networks (GANs) contain two neural networks; one is generator and the other is discriminator. In the generator neural networks, the fake images are created from the given data set. At the same time, discriminator neural networks are used to evaluate the images which are synthesized by the generator and check its authenticity. The important problems of deep fake are so harmful due to defamation of individual character and assassination and spreading fake news in the society.

There are many such issues in the existing approaches in terms of inefficiency in detecting the deep fake images, high error rate, high consumption time also high and inaccuracy in accessing the data. This work FF-LBPH-DBN focuses mainly on the minimization of computation and the application for various metrological parameters in an efficient way. Table 1 shows the survey based on detection of the fake images (Vivek et al., 2018).

Methodology

The proposed face recognition and fake detection is based on the deep learning technique of the fisherface using the Local Binary Pattern Histogram (FF-LBPH). The accurate detection of deep fake image system consists of four phases such as (i) pre-processing, (ii) dimensionality reduction of image (iii) feature extraction and (iv) classification. This architecture of proposed work diagram is given in Fig. 1.

Figure 1 shows the pre-processing phase which includes resizing of images, removal of noise and normalization. For the improvement of feature extraction and classification process, the dimensionality reduction of face image is used by the fisherface algorithm (LBPH).

Pre-Processing

For classifying the deep fake image pre-processing is needed which enhancing the image for further processing. The steps involved in the phase of pre-processing are given in Fig. 2.

Figure 2 shows the three stages of pre-processing namely, resizing of image, removal of noise and normalization.

Dimensionality reduction using proposed fisherface-LBPH

Dimensionality reduction is an important step to reduce the dimension of the input image into low dimensional space. The proposed work was based on fusion of fisherface with Local Binary Pattern Histogram (FF-LBPH) which was utilized in the reduction of face space dimension. The fusion of fisherface with Linear Binary Pattern Histogram (LBPH) was implemented in the proposed study.

Fisherface

The particular technique is based on Fisher’s Linear Discriminant Analysis (FLDA). The main advantage of the fisherface algorithm is faster in execution when compared to the eigenface technique. It is prominent for low error rates and also it works efficiently in various illuminations with different facial expressions. Steps involved in the fisherface algorithm are given below,

——————————————————————————————————

Algorithm 1: Dimensionality reduction in feature extraction Fisher face (Proposed)

——————————————————————————————————

Input: Face Image from the data set

Output: Dimensionality reduction in feature extraction

Step 1: Assume that size of the square face image with height = width = N and img is the number of images in the database.

Step 2: Select sample images form the database $\left\{\vec{a},\vec{b},\dots ,\vec{e}\right\}$ and class scatter $c=\left\{x_1,x_2,\dots ,x_n\right\}$

face image 1 = $\left\{\begin{array}{c}\hfill \begin{array}{c}\hfill a_1\hfill \\ \hfill a_2\hfill \end{array}\hfill \\ \hfill a_3\hfill \\ \hfill ⋮\hfill \\ \hfill a_{N^2}\hfill \end{array}\right\}$; face image 2 = $\left\{\begin{array}{c}\hfill \begin{array}{c}\hfill b_1\hfill \\ \hfill b_2\hfill \end{array}\hfill \\ \hfill b_3\hfill \\ \hfill ⋮\hfill \\ \hfill b_{N^2}\hfill \end{array}\right\};$face image 3 = $\left\{\begin{array}{c}\hfill \begin{array}{c}\hfill c_1\hfill \\ \hfill c_2\hfill \end{array}\hfill \\ \hfill c_3\hfill \\ \hfill ⋮\hfill \\ \hfill c_{N^2}\hfill \end{array}\right\}$;

face image 4 = $\left\{\begin{array}{c}\hfill \begin{array}{c}\hfill d_1\hfill \\ \hfill d_2\hfill \end{array}\hfill \\ \hfill d_3\hfill \\ \hfill ⋮\hfill \\ \hfill d_{N^2}\hfill \end{array}\right\}$; face image 5 = $\left\{\begin{array}{c}\hfill \begin{array}{c}\hfill e_1\hfill \\ \hfill e_2\hfill \end{array}\hfill \\ \hfill e_3\hfill \\ \hfill ⋮\hfill \\ \hfill e_{N^2}\hfill \end{array}\right\}$; face image 6 = $\left\{\begin{array}{c}\hfill \begin{array}{c}\hfill f_1\hfill \\ \hfill f_2\hfill \end{array}\hfill \\ \hfill f_3\hfill \\ \hfill ⋮\hfill \\ \hfill f_{N^2}\hfill \end{array}\right\}$

Step 3: Calculate the average of all faces by using: (2)${\displaystyle \vec{m}=\frac{1}{img}\left[\begin{array}{c}\hfill a_1+b_1+\cdots +f_1\hfill \\ \hfill a_2+b_2+\cdots +f_2\hfill \\ \hfill ⋮⋮⋮\hfill \\ \hfill a_{N^2}+b_{N^2}+\cdots +f_{N^2}\hfill \end{array}\right]}$ where img = 6

Step 4: Calculate the average face of each image in the data set

${\overrightarrow{img}}_1=\frac{1}{2}\left[\begin{array}{c}\hfill a_1+b_1\hfill \\ \hfill a_2+b_2\hfill \\ \hfill ⋮⋮\hfill \\ \hfill a_{N^2}+b_{N^2}\hfill \end{array}\right]$; ${\overrightarrow{img}}_2=\frac{1}{2}\left[\begin{array}{c}\hfill c_1+d_1\hfill \\ \hfill c_2+d_2\hfill \\ \hfill ⋮⋮\hfill \\ \hfill c_{N^2}+d_{N^2}\hfill \end{array}\right]$; ${\overrightarrow{img}}_3=\frac{1}{2}\left[\begin{array}{c}\hfill e_1+f_1\hfill \\ \hfill e_2+f_2\hfill \\ \hfill ⋮⋮\hfill \\ \hfill e_{N^2}+f_{N^2}\hfill \end{array}\right]$Step 5: Subtract the average face of each image from the training images (3)${\displaystyle {\overrightarrow{img}}_{1m}=\left[\begin{array}{c}\hfill a_1-{img}_{11}\hfill \\ \hfill a_2-{img}_{12}\hfill \\ \hfill ⋮⋮\hfill \\ \hfill a_{N^2}-{img}_{1N^2}\hfill \end{array}\right];{\overrightarrow{img}}_{2m}=\left[\begin{array}{c}\hfill b_1-{img}_{11}\hfill \\ \hfill b_2-{img}_{12}\hfill \\ \hfill ⋮⋮\hfill \\ \hfill b_{N^2}-{img}_{1N^2}\hfill \end{array}\right];{\overrightarrow{img}}_{3m}=\left[\begin{array}{c}\hfill c_1-{img}_{21}\hfill \\ \hfill c_2-{img}_{22}\hfill \\ \hfill ⋮⋮\hfill \\ \hfill c_{N^2}-{img}_{2N^2}\hfill \end{array}\right]{\overrightarrow{img}}_{4m}=\left[\begin{array}{c}\hfill d_1-{img}_{21}\hfill \\ \hfill d_2-{img}_{22}\hfill \\ \hfill ⋮⋮\hfill \\ \hfill d_{N^2}-{img}_{2N^2}\hfill \end{array}\right];{\overrightarrow{img}}_{5m}=\left[\begin{array}{c}\hfill e_1-{img}_{31}\hfill \\ \hfill e_2-{img}_{32}\hfill \\ \hfill ⋮⋮\hfill \\ \hfill e_{N^2}-{img}_{3N^2}\hfill \end{array}\right];{\overrightarrow{img}}_{6m}=\left[\begin{array}{c}\hfill f_1-{img}_{41}\hfill \\ \hfill f_2-{img}_{42}\hfill \\ \hfill ⋮⋮\hfill \\ \hfill f_{N^2}-{img}_{4N^2}\hfill \end{array}\right]}$ Step 6: Create scatter matrix sm₁,   sm₂,  sm₃, sm₄ (4)${\displaystyle {sm}_1=\left({\overrightarrow{img}}_{1m}{\overrightarrow{img}}_{1m}^T+{\overrightarrow{img}}_{2m}{\overrightarrow{img}}_{2m}^T\right)}$ (5)${\displaystyle {sm}_2=\left({\overrightarrow{img}}_{3m}{\overrightarrow{img}}_{3m}^T+{\overrightarrow{img}}_{4m}{\overrightarrow{img}}_{4m}^T\right)}$ (6)${\displaystyle {sm}_3=\left({\overrightarrow{img}}_{5m}{\overrightarrow{img}}_{5,m}^T+{\overrightarrow{img}}_{6m}{\overrightarrow{img}}_{6m}^T\right)}$ Step 7: Construct a scatter matrix within the class s_mw = sm₁ + sm₂ + sm₃.

Step 8: Construct the scatter matrix between class ${\displaystyle s_{mb}=2\left({\overrightarrow{img}}_1-\vec{m}\right){\left({\overrightarrow{img}}_1-\vec{m}\right)}^T+2\left({\overrightarrow{img}}_2-\vec{m}\right){\left({\overrightarrow{img}}_2-\vec{m}\right)}^T}$ (7)${\displaystyle +2\left({\overrightarrow{img}}_3-\vec{m}\right){\left({\overrightarrow{img}}_3-\vec{m}\right)}^T}$ Step 9: Compute the vector vec_img and the columns of vec_img contain eigen vector values for $s_{mw}^{-1}{s}_{mb}$ . Here s_mw is minimized; s_mb is maximized by using: (8)${\displaystyle {vec}_{img}=\left|\frac{{vec}_{img}^T{s}_{mb}}{{vec}_{img}^T{s}_{mw}}\right|}$ Generally, it can be defined by the decomposition of eigen value and it is represented as: (9)${\displaystyle s_{mb}{vec}_{img}=s_{mw}{vec}_{img}\mathrm{\Lambda }}$ Where, vec_img is eigen vector matrix and Λ are eigen values in the diagonal matrix. Eigen vectors vec_img are associated with eigen values of non-zero which are the fisherfaces.

Step 10: Normalization of the Equation

Step 11: Evaluate the weight for training image in the dataset in the normalized fisherface.

Step 12: Extracting features using dimensionality reduction of features so as to obtain face identification.

Algorithm 1 is an improvement version of eigen faces which include Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). In order to get a sub-space and to maximize the variability within classes and between the classes of scatter matrix.

Deepfake detection using classifier

Background of DeepFake

In the synthetic media, deepfakes are replacing the existing face image with image of someone else. It uses Generative Adversarial Networks (GANs) for manipulating the faces. The facial manipulation contains three phases namely face synthesis, face swap and facial attributes and expressions.

Face Synthesis.

Using GAN in this phase replaces the real face with the fake image. The best approach used in this phase is StyleGAN. In the StyleGAN unsupervised training process is implemented and it generates the images with variations such as hair, freckles, etc. It also enables the synthetic controls of the image.

Face swap.

In the face manipulation, face swap is one of the popular techniques. It is used to detect the image or video of a person fake or real after swapping its face in the image. The most popular database which contains real and fake videos are FaceForensics++. From the database, the fakevideos are used with the help of FaceSwap computer graphics concept and the other deep learning techniques such as DeepFakeFaceSwap.

Facial attributes and expressions.

Facial attribute modifications include color of skin, hair, age, gender. Similarly, change of face expressions include sad, happy, anger and so on. These are called as manipulation of facial attributes and its expressions. Moreover, the most popular mobile app is FaceApp. It uses StarGAN methos for performing the image-to image translation (Tolosana et al., 2020).

Deepfake detection using classifier of DBN-RBM

Deepfake is a technique which uses the Generative Adversarial Networks (GANs) for generating fake images. Deepfake detection is based on the classifier algorithm Deep Belief Network (DBN) which is used to classify fake images from authentic image. DBN technique consists of three layers such as input, hidden and output layers. In addition to that, deep learning network contains stacked hidden layers and it is the extension of the neural network. DBN consists of one visible layer and multiple hidden layers. Transmission of input face image through visible layer to hidden layer is activated through sigmoid function based on the RBM learning rule (Hinton, Osindero & Teh, 2006). It is based on Restricted Boltzmann Machine (RBM) and DBN is acted with RBM which communicates with the previous layer and the subsequent layers in the DBN network. The architecture of DBN with RBM is shown in Fig. 3.

Figure 3 shows the architecture of DBN that consists of two stacked RBMs. RBM1 consists of visible layer and hidden layer 1, RBM2 consists of hidden layer 1 and hidden layer 2. In this architecture, the input face image is trained in the DBN based RBM classifier with the learning rule. The parameters which are used in the DBN architecture contain weight values between visible layer and hidden layer, value of bias and neuron states. Sigmoid function is applied for the transformation of neuron values from previous layer to the next layer using: (13)${\displaystyle P\left({sigmo}_i=1\right)=\frac{1}{1+\exp \mathrm{}\left(-b_i-\sum _j{sigmo}_j{w}_{ij}\right)}.}$ Bias and weight of all neurons are initialized in the RBM layer. In the training, the input face image consists of positive and negative phase. In the positive phase, input data is transformed from visible layer to hidden layer and negative phase transforms the input data from hidden layer to visible layer. In the positive and negative phases, individual activation function is calculated by using Eqn and that are defined as. (14)${\displaystyle P\left(v_i=1|h\right)=sigmo\left(-b_i-\sum _j{h}_j{w}_{ij}\right)}$ (15)${\displaystyle P\left(h_i=1|v\right)=sigmo\left(-c_i-\sum _j{h}_j{w}_{ij}\right)}$ where, v_i isthe visible layer; h_i is the hidden layer and w_ij is the weight value.

This process is repeated and updated the weight value in the DBN architecture, until the maximum number of epochs is reached. The training process continued and the parametric values are optimized using: (16)${\displaystyle update\left(w_{ij}+\frac{\eta }{2}\times \left(positive\left(E_{ij}\right)-negative\left(E_{ij}\right)\right)\right)}$ where,

$positive\left(E_{ij}\right)$-Positive statistics of edge E_ij = p(h_j = 1|v)

$negative\left(E_{ij}\right)$-Positive statistics of edge E_ij = p(v_j = 1|h)

η- learning rate

Using the above procedure, RBM is trained and the same process is repeated until all RBM get trained. By using the proposed work of dimensionality reduction in feature extraction, fisherface-LBPH was used for feature extraction with DBN-RBM classifier and it was used to recognize and differentiate the fake image and the real image.

Result & Discussions

The proposed deep learning dimensionality reduction in feature extraction fisherface-LBPH evaluated the real and fake images in the public dataset. The public datasets used for deepfake detection were FFHQ, 100K-Faces, DFFD, CASIA-WebFace .

Specificity

It is used to evaluate the rate between True Negative (TN) and True Positive (TP) (19)${\displaystyle Specificty=\frac{TN}{TN+FP}X100}$ The error rate is given below: (20)${\displaystyle PSNR=20{\log }_{10}\left(\frac{{255}^2}{MAE}\right)}$ (21)${\displaystyle MAE=\frac{1}{MN}\sum _{i=1}^M\sum _{j=1}^N\left|X\left(i,j\right)-Y\left(i,j\right)\right|}$ (22)${\displaystyle RMSE=\sqrt{\frac{1}{N}}\sum _{i=1}^N{\left(X_i-\widehat{X_i}\right)}^2}$ (23)${\displaystyle SNR\left(db\right)=20log\left(\frac{V_{RMS\left(Signal\right)}}{V_{RMS\left(Noise\right)}}\right)}$ Table 2 shows that accuracy rate of proposed work that is compared with the different data set.

Table 3 shows the accuracy of various algorithm with the proposed work and it is implemented in various public available data set such as FFHQ, 100K-Faces, DFFD and CASIA-WebFace. The accuracy rate of proposed work FF-LBPH-DBN was high (98.82%) in the dataset of CASIA-WebFace image dataset. The next position in terms of accuracy rate is 97.82% for DFFD dataset. Table 4 shows error detection rate of proposed work in various data set.

Table 3:

Performance comparison of proposed methods with different datasets in terms of accuracy.

Methods	Datasets
	FFHQ	100K-Faces	DFFD	CASIA-WebFace
SVM	82.5	70.12	84.43	85.25
LDA	86.32	78.11	88.32	84.52
KNN	88.15	80.21	87.01	91.75
CNN	89.23	82.45	88.55	86.12
Proposed FF-LBPH-DBN	94.92	95.55	97.82	98.82

DOI: 10.7717/peerjcs.881/table-3

Table 4:

Performance comparison of proposed methods with different datasets in terms of error detection rate.

Methods	Datasets
	FFHQ	100K-Faces	DFFD	CASIA-WebFace
SVM	15.37	26.81	13.97	12.48
LDA	14.25	24.02	13.75	12.82
KNN	12.33	16.56	12.78	10.19
CNN	12.23	15.25	12.78	10.25
Proposed FF-LBPH-DBN	9.12	11.25	9.04	7.06

DOI: 10.7717/peerjcs.881/table-4

Table 4, shows the error rate of various algorithm in different data sets. The proposed work FF-LBPH-DBN got minimum error rate of 7.06 in the data set CASIA-WebFace data set. Table 5 shows the sensitivity, specificity performance comparison using various techniques namely SVM, LDA, KNN, CNN. The proposed work FF-LBPH-DBN with various datasets of FFHQ, 100K-Faces, DFFD, CASIA-WebFace were provided for better understanding.

Table 5:

Sensitivity and specificity in different data sets.

Data Sets	Sensitivity %					Specificity %
	SVM	LDA	KNN	CNN	FF-LBPH-DBN	SVM	LDA	KNN	CNN	FF-LBPH-DBN
FFHQ	85.3	796	83.9	82.4	89.67	82.75	85.7	81.45	86.78	91.22
100K-Faces	84.2	81	83.8	81.8	86.88	83.75	85.9	89.54	86.89	92.45
DFFD	82.9	86.3	82.7	80.9	88.9	86.75	86.9	88.45	84.56	93.76
CASIA-WebFace	89.2	81.2	89	87	91.35	85.78	88.4	85.12	87.91	94.35

DOI: 10.7717/peerjcs.881/table-5

Table 5 shows the sensitivity, specificity that provide best performance for FF-LBPH-DBN algorithm compared to the existing algorithms and various data sets of FFHQ, 100K-Faces, DFFD, CASIA-WebFace. Whereas, FF-LBPH-DBN of proposed work got sensitivity score as 89.67% in FFHQ data set, 86.88% in 100K-Faces data set, 88.9% in DFFD data set and 91.35% in CASIA-WebFace data set. Similarly for Specificity of proposed work FF-LBPH-DBN got 91.22% in FFHQ data set, 92.45% in 100K-Faces data set, 93.76% in DFFD data set and 94.35% in CASIA-WebFace data set. The deepfake detection of face image in the aspects of Equal Error Rate (EER) and AUC was done on the datasets of FFHQ, 100K-Faces, DFFD, CASIA-WebFace. These datasets were used in both training and testing process by deepfake detection classifier methods namely, deepfake, face swap, face synthesis with the proposed work of FF-LBPH-DBN model. Table 4 shows the performance of deepfake detection in the aspects of Equal Error Rate (EER) and AUC on various datasets.

Table 6 shows the exact recognition of real and fake images for deepfake, faceswap and face synthesis methods. In the proposed work, the dataset of CASIA-WebFace had attained better performance in the methods of deepfake with 0.978 in AUC and 7.21 in EER, faceswap with 0.986 in AUC and 9.56 in EER and facesynthesis with 0.788 in AUC and 12.32 in EER. Figure 4 shows the error rate value that is calculated based on its accuracy using the Eq22–Eq25.

Table 6:

EER and AUC on deepfake detection methods.

Data Sets	Deepfake		Face swap		Face Synthesis
	AUC	EER	AUC	EER	AUC	EER
FFHQ	0.948	13.33	0.918	13.65	0.726	16.49
100K-Faces	0.975	9.57	0.954	10.76	0.772	19.51
DFFD	0.969	12.45	0.944	12.78	0.714	15.67
CASIA-WebFace	0.978	7.21	0.986	9.56	0.788	12.32

DOI: 10.7717/peerjcs.881/table-6

From Fig. 4, it is observed that PSNR value must be increased and MAE value must be decreased for the best detection of fake face image and real face image. The proposed approach provided better error rate value with the base of accuracy. In the proposed work the value of PSNR was increase and the value of MAE got decreased when compared to the other existing techniques. Figure 5 shows the computation time for various algorithms.

In Fig. 5, it is revealed that the proposed algorithm of FF-LBPH-DBN needs less computation time when compared to the other existing algorithms. The analysis of training and testing of dataset in the face image had produced validation face input data in the terms of accuracy and loss metric information in the deepfake face image dataset. The proposed work of FF-LBPH-DBN model in epochs is shown in Fig. 6.

It is observed in Fig. 6 that the proposed methods FFHQ, 100K-Faces, DFFD, CASIA-WebFace of dataset provide less validation loss and good validation accuracy for FF-LBPH-DBN model.

Conclusion

In this paper, the fisherface Linear binary pattern histogram using the DBN classifier (FF-LBPH DBN) technique was implemented as a detection technique for deepfake images. The proposed work was faster in execution and the detection of fake image and real image was very effective. Deepfake face image manipulations were analyzed using FF-LBPH DBN model and it also produced high level of accuracy. The pre-processing work had been done using Kalman filter for the detection of the fake images in a fine-tuned recognition. In order to get less execution of time, the dimensionality reduction of features were utilized using a fusion of the fisherface-LBPH algorithm. It helped in detecting the fake face image which in turn could prevent the individuals from being defamed unknowingly. From the results, it was concluded that the proposed work FF-LBPH had produced better detection and analysis of deepfake face image. The accuracy rate of proposed work FF-LBPH-DBN had attained a value of 98.82% in the CASIA-WebFace image dataset. The next position in terms of accuracy rate was 97.82% for the DFFD dataset. For future work, it may be extended up to various classifiers and use of different distance metric measures for detecting the deepfake face image.

Supplemental Information